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Streams:

  • gsoc 2022 (1 topic)
    • -

    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 29 2024 at 00:41 UTC

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    Stream: signups (3830 messages, latest: Jul 27 2024 at 23:11) +
  • signups (3833 messages, latest: Jul 28 2024 at 23:59)
  • Zulip updates (8 messages, latest: Jul 26 2024 at 00:23)
  • GSoC (4 messages, latest: Mar 28 2024 at 17:23)
  • new streams (3 messages, latest: Jun 09 2023 at 15:12)
    • @@ -31,5 +31,5 @@

    Topics:

  • welcome (1 message, latest: Oct 25 2017 at 04:03)
    • -

    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +     +

    view this post on Zulip Notification Bot (Jul 28 2024 at 12:45):

    +

    Michał joined this organization.

    + + + + +

    view this post on Zulip Notification Bot (Jul 28 2024 at 19:08):

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    Mark Redin Panisales joined this organization.

    + + + + +

    view this post on Zulip Notification Bot (Jul 28 2024 at 23:59):

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    SuperStarGamer joined this organization.

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    Last updated: Jul 29 2024 at 00:41 UTC

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    Topics:

    -

    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +     +

    view this post on Zulip Sean (Jul 28 2024 at 23:59):

    +

    Can confirm, build appears to be working and passes make check on the cfarm 188 machine (Linux x86_64, openSUSE Leap 15.5)

    + + + +

    Last updated: Jul 29 2024 at 00:41 UTC

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    +

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    Last updated: Jul 28 2024 at 00:43 UTC

    +     +

    view this post on Zulip Sean (Jul 28 2024 at 23:57):

    +

    Still don't yet have the delta, but here's full run results I got on Linux w/ main:

    +
    Summary
+=======
+Converted: 96.9%  ( 9923 of 10245 objects, 40 files )
+   Passed: 9923   ( 9974 NMG 10231 BoT 10116 Brep )
+   Failed: 303   ( 243 NMG 12 BoT 126 Brep )
+  Timeout:  19   ( 28 NMG 2 BoT 3 Brep )
+ NMG rate: 97.4%  ( 9974 of 10245 )
+ BoT rate: 99.9%  ( 10231 of 10245 )
+Brep rate: 98.7%  ( 10116 of 10245 )
+Prim rate: 99.7%  ( 7029 of 7050 )
+ Reg rate: 95.1%  ( 2316 of 2436 )
+  Elapsed: 61697.0 seconds
+  Average: 6.0 seconds per object
+
    + + + +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Topics:

  • onboarding (1 message, latest: Oct 25 2017 at 04:03)
    • -

    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    topic demonstration (2 messages, latest: Oct 25 2017 at 04:03) -

    Last updated: Jul 28 2024 at 00:43 UTC

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    Topics:

  • New Interface (5 messages, latest: Dec 01 2017 at 15:01)
    • -

    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

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    Last updated: Jul 28 2024 at 00:43 UTC

    +     +

    view this post on Zulip fall Rainy (Jul 28 2024 at 11:42):

    +

    This is really possible. I recently saw some algorithms for interpolation between multiple pictures. Maybe we can predict in key directions and then make differences in other directions.

    + + + + +

    view this post on Zulip fall Rainy (Jul 28 2024 at 13:27):

    +

    If the direction is contant, network is learning a func like:
    +2024-07-28-212551.png

    +
    + + + + +

    view this post on Zulip fall Rainy (Jul 28 2024 at 13:29):

    +

    Interpolated fits between different perspectives have been done before:
    +2024-07-28-212847.png
    +from VIINTER: View Interpolation with Implicit Neural Representations of Images

    +
    + + + + +

    view this post on Zulip Matteo Balice (Jul 28 2024 at 14:29):

    +

    Sean ha scritto:

    +
    +

    That's sounding a lot better. I think it'll still need to get into the 99% realm, but that's a distinct improvement. What's that 94%-95% look like?

    +
    +

    I have not rendered any frame for gaussian splatting network yet because if the percentage are under 0.97 - 0.98, the predicted image is very far from the true one. I prefer first to achieve better results and then plotting it.

    + + + + +

    view this post on Zulip Matteo Balice (Jul 28 2024 at 14:35):

    +

    Sean ha scritto:

    +
    +

    One thought I had, and it's obviously in a different direction but maybe applicable is what if we try integrating over multiple networks. That is, use the simple network they demonstrated works well for a single view, but then lets create one for 32 (or 32768) views, get estimated in-hit points from all of them, and integrate spatially.

    +
    +

    This idea is not far from the idea of grid encoding of direction which I have already tried without any improvement. In that case I used embeddings for each direction, you suggest using a network for each direction. I can try it. :thumbs_up:

    + + + + +

    view this post on Zulip Matteo Balice (Jul 28 2024 at 14:54):

    +

    fall Rainy ha scritto:

    +
    +

    Interpolated fits between different perspectives have been done before:
    +2024-07-28-212847.png
    +from VIINTER: View Interpolation with Implicit Neural Representations of Images

    +
    +

    This could work but it means that we have to change the sampling to fixed view sampling. I have some doubts about the number of views we should sample because:
    +If we choose for example 1 million rays in the training set and we choose a reasonable number of 1000 rays for each view this means that we have a total of 1000 views.
    +If we distribute uniformly these views around the bounding sphere it means that, remember that theta goes from 0 to pi and phi goes from 0 to 2pi:
    +In my case I can use half of phi because in my case the problem is simpler and the direction is invariant to orientation (so I have theta that goes from 0 to pi and phi that goes from 0 to pi):
    +Theta x phi = 3,14 x 3,14 = 9,8596 -> total space
    +Uniform distribution of views:
    +9,8596/1000 views = 0,009859 rad = 0,56° between two views.
    +I think it is a reasonable error and it can be easily interpolated.
    +In your case, however, you can't reduce the range of phi (because rgb is not invariant to orientations), so you will have:
    +Theta x phi = 3,14 x 6,28 = 19,719 -> total space
    +19,719/1000 views = 0,0197 rad = 1,128° between two views.
    +An error of 1° is reasonable also in your case and if my calculations are exact, it could work even in your case but the error is the double of mine.

    + + + + +

    view this post on Zulip Matteo Balice (Jul 28 2024 at 14:57):

    +

    I believe we should try this approach using NIF architecture and then interpolating them.

    + + + + +

    view this post on Zulip Matteo Balice (Jul 28 2024 at 15:10):

    +

    @Sean I have a doubt about rays and pixels: if we want to render a frame of 100x100 it means that the algorithm sample for each pixel a ray (for a total of 10k rays) or is it different?

    + + + + +

    view this post on Zulip Sean (Jul 28 2024 at 23:13):

    +

    Matteo Balice said:

    +
    +

    I have not rendered any frame for gaussian splatting network yet because if the percentage are under 0.97 - 0.98, the predicted image is very far from the true one. I prefer first to achieve better results and then plotting it.

    +
    +

    That is absolutely not best practice and not recommended to ignore the predicted images solely based on having low percentages. Not looking gives you no information. Looking may give no information, or may provide helpful clues as to what isn't encoding well.

    +

    It can be high frequency detail, it can be a straight up bug where values are simply shifted, it can be low frequency undulations, and more. It's not in your interest to ignore them even if 19 times out of 20 it's just a "drunk wet mess".

    + + + + +

    view this post on Zulip Sean (Jul 28 2024 at 23:18):

    +

    Matteo Balice said:

    +
    +

    I believe we should try this approach using NIF architecture and then interpolating them.

    +
    +

    I will just reiterate what we'd discussed earlier, that there should be two different approaches being taken (in general), or even better two different goals (e.g., 3d shape vs 2d image). There is value in exploring the same method with different implementation detail (on the off chance there is some detail that matters more than anticipated).

    + + + + +

    view this post on Zulip Sean (Jul 28 2024 at 23:22):

    +

    Matteo Balice said:

    +
    +

    This idea is not far from the idea of grid encoding of direction which I have already tried without any improvement. In that case I used embeddings for each direction, you suggest using a network for each direction. I can try it. :thumbs_up:

    +
    +

    It's not far off, but the separate networks is the key. AMD really proved that surface illumination can be almost perfectly encoded for a given view. Maybe if we were to first reproduce their research, that would give more confidence, but lacking that it's not terribly unexpected.

    +

    Now that said, I don't think there's a whole lot of difference with random rays in random dirs -- naively I think that can work with the right network and right amount of training. Remains to be proven though. The idea with the 32+ grid views, however, is a compromise, banking on the notion that a single view should converge that view. In other analysis work we're involved with, there's mathematical proofs that lend evidence towards 32 views being a sweet spot approximation for complete random, converging much faster than pure random.

    + + + + +

    view this post on Zulip Sean (Jul 28 2024 at 23:25):

    +

    Matteo Balice said:

    +
    +

    fall Rainy ha scritto:

    +
    +

    Interpolated fits between different perspectives have been done before:
    +2024-07-28-212847.png
    +from VIINTER: View Interpolation with Implicit Neural Representations of Images

    +
    +

    This could work but it means that we have to change the sampling to fixed view sampling. I have some doubts about the number of views we should sample because:
    +If we choose for example 1 million rays in the training set and we choose a reasonable number of 1000 rays for each view this means that we have a total of 1000 views.
    +If we distribute uniformly these views around the bounding sphere it means that, remember that theta goes from 0 to pi and phi goes from 0 to 2pi:
    +In my case I can use half of phi because in my case the problem is simpler and the direction is invariant to orientation (so I have theta that goes from 0 to pi and phi that goes from 0 to pi):
    +Theta x phi = 3,14 x 3,14 = 9,8596 -> total space
    +Uniform distribution of views:
    +9,8596/1000 views = 0,009859 rad = 0,56° between two views.
    +I think it is a reasonable error and it can be easily interpolated.
    +In your case, however, you can't reduce the range of phi (because rgb is not invariant to orientations), so you will have:
    +Theta x phi = 3,14 x 6,28 = 19,719 -> total space
    +19,719/1000 views = 0,0197 rad = 1,128° between two views.
    +An error of 1° is reasonable also in your case and if my calculations are exact, it could work even in your case but the error is the double of mine.

    +
    +

    Again, I would just caution whether we're following research that is attempting to capture shape in the embedding or whether the goal is capturing the shape just barely enough that color, i.e., a visual image can be constructed that "looks good enough". On quick read, that VINTER paper appears to be the latter, but I'd have to read it in more detail.

    + + + + +

    view this post on Zulip Sean (Jul 28 2024 at 23:28):

    +

    As for total number of views, I think you could try as coarse as 45-degree increments. Resolution will need to be as fine as the smallest detail, which depends on the model size and detail complexity. I'd personally start at 1024x1024, about 1M per view.

    + + + + +

    view this post on Zulip Sean (Jul 28 2024 at 23:29):

    +

    That 1024^2 resolution at 45-degree probably means something like 256 or 512 resolution alignment, whatever that cell size resolves to.

    + + + +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    +

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    +

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +     +

    view this post on Zulip Daniel Rossberg (Jul 28 2024 at 15:08):

    +

    Louis Googl said:

    +
    +

    regarding the ELEMENT_BEAM do we want to draw it as a line or we take the section and extrude it? in LS-PrePost they draw it as a line. the beams in the following image have an I section (section 1 in the second image) but they are represented as a line.
    +image.png
    +image.png

    +
    +

    For BRL-CAD, we need all elements as solids. I.e., they should have the real beam shape, if feasible. If that's too complicated or the real shape isn't well defined, a cuboid with corresponding line-of-sight value would be possible.

    + + + + +

    view this post on Zulip Daniel Rossberg (Jul 28 2024 at 15:19):

    +

    Louis Googl said:

    +
    +

    another thing I am working on is the distinction between different commands and a command with options
    +for example *PART and *PART_ADAPTIVE_FAILURE are two different commands, but *PART and *PART_CONTACT are the same *PART command with the option CONTACT, also because the order of the options defines what the following cards (lines) contains, so I think we are going to need a vector of strings to hold the options and we need to combine the logic to check partLinesRead and the options vector to know what card we are reading. This will not be necessary for all the options as most of them aren't related to the geometry.
    +image.png
    +image.png

    +
    +

    In the converter, the distinction is done with the KState enumeration. This addresses the real different stuff. E.g., PART and PART_INERTIA are in fact the same, PART_ADAPTIVE_FAILURE is something different.

    +

    Regarding the options, I'll review your proposal in the PR.

    + + + + +

    view this post on Zulip Louis Googl (Jul 28 2024 at 15:25):

    +

    Daniel Rossberg said:

    +
    +

    For BRL-CAD, we need all elements as solids. I.e., they should have the real beam shape, if feasible. If that's too complicated or the real shape isn't well defined, a cuboid with corresponding line-of-sight value would be possible.

    +
    +

    is there some example code I can look at?.
    +for a beam that have a T section we can devided to two arbs? or is there a methode for extrusion from the section?
    +image.png

    +
    + + + + +

    view this post on Zulip Daniel Rossberg (Jul 28 2024 at 15:47):

    +

    Hmm, theoretically, we have the extrude primitive, which extrudes a 2D sketch along a line. Using arbs would be possible too (overlaps wouldn't matter).

    + + + + +

    view this post on Zulip Daniel Rossberg (Jul 28 2024 at 15:55):

    +

    Regarding the options: Do we have a kind of stack there? E.g., for every option a global, section and element value? WE could think about crating an option object, which reacts on the enter section, enter element, exit element, and exit section events. (I don't know, if we can really say when we "exit" something, maybe we can see the "enter"s only.)

    + + + + +

    view this post on Zulip Louis Googl (Jul 28 2024 at 16:24):

    +

    the option card or line follows directly under the command say we have Element_Beam_Section card3 will be there and card2 wouldn't. this is the way to know that you are exiting an element, number of lines is equal to the required card pluse the number of options.
    +image.png

    +
    + + + + +

    view this post on Zulip Daniel Rossberg (Jul 28 2024 at 17:29):

    +

    Louis Googl said:

    +
    +

    another issue is that an option can override properties that are defined in other places, for example if we have *SECTION_BEAM with a certain thickness and then *ELEMENT_BEAM with the option THICKNESS i.e *ELEMENT_BEAM_THICKNESS the latter command overrides the thickness defined in the former command. I am trying to find a way to protect the information provided by the superiour command so it doesn't change if the *SECTION_BEAM comes after *ELEMENT_BEAM_THICKNESS

    +
    +

    I understood that *SECTION_BEAM and *ELEMENT_BEAM can be in any order anywhere in the data. The link will be created when parsing KData via the IDs. I.e., the thicknesses have to be stored in the data and evaluated when parsing it.

    + + + + +

    view this post on Zulip Louis Googl (Jul 28 2024 at 17:59):

    +

    yes, now I understand what you meant. exactly the the ID is the link. but after storing the thickness from ELMENT_BEAM_THICKNESS it needs to be protected from change, and if it's taken from SECTION_BEAM it should be modifiable.

    + + + + +

    view this post on Zulip Daniel Rossberg (Jul 28 2024 at 18:41):

    +

    You need a kind of std::map<std::string, std::string> or std::map<std::string, double> for the options:

    +
    thickness := default
+
+if SECTION.options[THICKNESS] then
+    thickness := SECTION.options[THICKNESS]
+
+if ELEMENT.options[THICKNESS] then
+    thickness := ELEMENT.options[THICKNESS]
+
    +

    (That's pseudo-code, of course.)

    + + + + +

    view this post on Zulip Sean (Jul 28 2024 at 23:03):

    +

    Examples of sketch+extrude are at https://brlcad.org/wiki/Sketch and https://brlcad.org/wiki/Extrude

    + + + +

    Last updated: Jul 29 2024 at 00:41 UTC

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    +

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    hello (1 message, latest: May 01 2019 at 13:10) -

    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

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    Topics:

  • stream events (1 message, latest: Nov 03 2019 at 16:59)
    • -

    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

    \ No newline at end of file diff --git a/stream/213268-GCI-2019/topic/stream.20events.html b/stream/213268-GCI-2019/topic/stream.20events.html index b7f07549f..cefe54979 100644 --- a/stream/213268-GCI-2019/topic/stream.20events.html +++ b/stream/213268-GCI-2019/topic/stream.20events.html @@ -15,5 +15,5 @@

    stream events (1 message, latest: Jan 18 2022 at 20:19) -

    Last updated: Jul 28 2024 at 00:43 UTC

    +

    Last updated: Jul 29 2024 at 00:41 UTC

    \ No newline at end of file diff --git a/stream/312913-gsoc-2022/topic/stream.20events.html b/stream/312913-gsoc-2022/topic/stream.20events.html index d2ab24b1d..c591eb63f 100644 --- a/stream/312913-gsoc-2022/topic/stream.20events.html +++ b/stream/312913-gsoc-2022/topic/stream.20events.html @@ -15,5 +15,5 @@

    Michał joined this organization.

    ", + "id": 454609444, + "sender_full_name": "Notification Bot", + "timestamp": 1722170727 + }, + { + "content": "

    Mark Redin Panisales joined this organization.

    ", + "id": 454652281, + "sender_full_name": "Notification Bot", + "timestamp": 1722193728 + }, + { + "content": "

    SuperStarGamer joined this organization.

    ", + "id": 454681537, + "sender_full_name": "Notification Bot", + "timestamp": 1722211144 } ] \ No newline at end of file diff --git a/zulip_json/104062-brlcad/build.json b/zulip_json/104062-brlcad/build.json index ceed5e7ea..957c07a03 100644 --- a/zulip_json/104062-brlcad/build.json +++ b/zulip_json/104062-brlcad/build.json @@ -5488,5 +5488,11 @@ "id": 454415256, "sender_full_name": "Sean", "timestamp": 1722051115 + }, + { + "content": "

    Can confirm, build appears to be working and passes make check on the cfarm 188 machine (Linux x86_64, openSUSE Leap 15.5)

    ", + "id": 454681576, + "sender_full_name": "Sean", + "timestamp": 1722211171 } ] \ No newline at end of file diff --git a/zulip_json/104062-brlcad/conversion.json b/zulip_json/104062-brlcad/conversion.json index 5dcd28b9e..128da3e36 100644 --- a/zulip_json/104062-brlcad/conversion.json +++ b/zulip_json/104062-brlcad/conversion.json @@ -28,5 +28,11 @@ "id": 451735092, "sender_full_name": "starseeker", "timestamp": 1721127931 + }, + { + "content": "

    Still don't yet have the delta, but here's full run results I got on Linux w/ main:

    \n
    Summary\n=======\nConverted: 96.9%  ( 9923 of 10245 objects, 40 files )\n   Passed: 9923   ( 9974 NMG 10231 BoT 10116 Brep )\n   Failed: 303   ( 243 NMG 12 BoT 126 Brep )\n  Timeout:  19   ( 28 NMG 2 BoT 3 Brep )\n NMG rate: 97.4%  ( 9974 of 10245 )\n BoT rate: 99.9%  ( 10231 of 10245 )\nBrep rate: 98.7%  ( 10116 of 10245 )\nPrim rate: 99.7%  ( 7029 of 7050 )\n Reg rate: 95.1%  ( 2316 of 2436 )\n  Elapsed: 61697.0 seconds\n  Average: 6.0 seconds per object\n
    ", + "id": 454681389, + "sender_full_name": "Sean", + "timestamp": 1722211056 } ] \ No newline at end of file diff --git a/zulip_json/111975-Google-Summer-of-Code/Neural.20Rendering.json b/zulip_json/111975-Google-Summer-of-Code/Neural.20Rendering.json index a8c3b7319..5d6cf4dc1 100644 --- a/zulip_json/111975-Google-Summer-of-Code/Neural.20Rendering.json +++ b/zulip_json/111975-Google-Summer-of-Code/Neural.20Rendering.json @@ -1660,5 +1660,89 @@ "id": 454545008, "sender_full_name": "Sean", "timestamp": 1722125811 + }, + { + "content": "

    This is really possible. I recently saw some algorithms for interpolation between multiple pictures. Maybe we can predict in key directions and then make differences in other directions.

    ", + "id": 454604656, + "sender_full_name": "fall Rainy", + "timestamp": 1722166937 + }, + { + "content": "

    If the direction is contant, network is learning a func like:
    \n    2024-07-28-212551.png

    \n
    ", + "id": 454612969, + "sender_full_name": "fall Rainy", + "timestamp": 1722173227 + }, + { + "content": "

    Interpolated fits between different perspectives have been done before:
    \n2024-07-28-212847.png
    \nfrom VIINTER: View Interpolation with Implicit Neural Representations of Images

    \n
    ", + "id": 454613104, + "sender_full_name": "fall Rainy", + "timestamp": 1722173380 + }, + { + "content": "

    Sean ha scritto:

    \n
    \n

    That's sounding a lot better. I think it'll still need to get into the 99% realm, but that's a distinct improvement. What's that 94%-95% look like?

    \n
    \n

    I have not rendered any frame for gaussian splatting network yet because if the percentage are under 0.97 - 0.98, the predicted image is very far from the true one. I prefer first to achieve better results and then plotting it.

    ", + "id": 454617963, + "sender_full_name": "Matteo Balice", + "timestamp": 1722176954 + }, + { + "content": "

    Sean ha scritto:

    \n
    \n

    One thought I had, and it's obviously in a different direction but maybe applicable is what if we try integrating over multiple networks. That is, use the simple network they demonstrated works well for a single view, but then lets create one for 32 (or 32768) views, get estimated in-hit points from all of them, and integrate spatially.

    \n
    \n

    This idea is not far from the idea of grid encoding of direction which I have already tried without any improvement. In that case I used embeddings for each direction, you suggest using a network for each direction. I can try it. :thumbs_up:

    ", + "id": 454618704, + "sender_full_name": "Matteo Balice", + "timestamp": 1722177347 + }, + { + "content": "

    fall Rainy ha scritto:

    \n
    \n

    Interpolated fits between different perspectives have been done before:
    \n2024-07-28-212847.png
    \nfrom VIINTER: View Interpolation with Implicit Neural Representations of Images

    \n
    \n

    This could work but it means that we have to change the sampling to fixed view sampling. I have some doubts about the number of views we should sample because:
    \nIf we choose for example 1 million rays in the training set and we choose a reasonable number of 1000 rays for each view this means that we have a total of 1000 views.
    \nIf we distribute uniformly these views around the bounding sphere it means that, remember that theta goes from 0 to pi and phi goes from 0 to 2pi:
    \nIn my case I can use half of phi because in my case the problem is simpler and the direction is invariant to orientation (so I have theta that goes from 0 to pi and phi that goes from 0 to pi):
    \nTheta x phi = 3,14 x 3,14 = 9,8596 -> total space
    \nUniform distribution of views:
    \n9,8596/1000 views = 0,009859 rad = 0,56° between two views.
    \nI think it is a reasonable error and it can be easily interpolated.
    \nIn your case, however, you can't reduce the range of phi (because rgb is not invariant to orientations), so you will have:
    \nTheta x phi = 3,14 x 6,28 = 19,719 -> total space
    \n19,719/1000 views = 0,0197 rad = 1,128° between two views.
    \nAn error of 1° is reasonable also in your case and if my calculations are exact, it could work even in your case but the error is the double of mine.

    ", + "id": 454620623, + "sender_full_name": "Matteo Balice", + "timestamp": 1722178467 + }, + { + "content": "

    I believe we should try this approach using NIF architecture and then interpolating them.

    ", + "id": 454620794, + "sender_full_name": "Matteo Balice", + "timestamp": 1722178630 + }, + { + "content": "

    @Sean I have a doubt about rays and pixels: if we want to render a frame of 100x100 it means that the algorithm sample for each pixel a ray (for a total of 10k rays) or is it different?

    ", + "id": 454622260, + "sender_full_name": "Matteo Balice", + "timestamp": 1722179406 + }, + { + "content": "

    Matteo Balice said:

    \n
    \n

    I have not rendered any frame for gaussian splatting network yet because if the percentage are under 0.97 - 0.98, the predicted image is very far from the true one. I prefer first to achieve better results and then plotting it.

    \n
    \n

    That is absolutely not best practice and not recommended to ignore the predicted images solely based on having low percentages. Not looking gives you no information. Looking may give no information, or may provide helpful clues as to what isn't encoding well.

    \n

    It can be high frequency detail, it can be a straight up bug where values are simply shifted, it can be low frequency undulations, and more. It's not in your interest to ignore them even if 19 times out of 20 it's just a \"drunk wet mess\".

    ", + "id": 454677762, + "sender_full_name": "Sean", + "timestamp": 1722208401 + }, + { + "content": "

    Matteo Balice said:

    \n
    \n

    I believe we should try this approach using NIF architecture and then interpolating them.

    \n
    \n

    I will just reiterate what we'd discussed earlier, that there should be two different approaches being taken (in general), or even better two different goals (e.g., 3d shape vs 2d image). There is value in exploring the same method with different implementation detail (on the off chance there is some detail that matters more than anticipated).

    ", + "id": 454678116, + "sender_full_name": "Sean", + "timestamp": 1722208680 + }, + { + "content": "

    Matteo Balice said:

    \n
    \n

    This idea is not far from the idea of grid encoding of direction which I have already tried without any improvement. In that case I used embeddings for each direction, you suggest using a network for each direction. I can try it. :thumbs_up:

    \n
    \n

    It's not far off, but the separate networks is the key. AMD really proved that surface illumination can be almost perfectly encoded for a given view. Maybe if we were to first reproduce their research, that would give more confidence, but lacking that it's not terribly unexpected.

    \n

    Now that said, I don't think there's a whole lot of difference with random rays in random dirs -- naively I think that can work with the right network and right amount of training. Remains to be proven though. The idea with the 32+ grid views, however, is a compromise, banking on the notion that a single view should converge that view. In other analysis work we're involved with, there's mathematical proofs that lend evidence towards 32 views being a sweet spot approximation for complete random, converging much faster than pure random.

    ", + "id": 454678542, + "sender_full_name": "Sean", + "timestamp": 1722208977 + }, + { + "content": "

    Matteo Balice said:

    \n
    \n

    fall Rainy ha scritto:

    \n
    \n

    Interpolated fits between different perspectives have been done before:
    \n2024-07-28-212847.png
    \nfrom VIINTER: View Interpolation with Implicit Neural Representations of Images

    \n
    \n

    This could work but it means that we have to change the sampling to fixed view sampling. I have some doubts about the number of views we should sample because:
    \nIf we choose for example 1 million rays in the training set and we choose a reasonable number of 1000 rays for each view this means that we have a total of 1000 views.
    \nIf we distribute uniformly these views around the bounding sphere it means that, remember that theta goes from 0 to pi and phi goes from 0 to 2pi:
    \nIn my case I can use half of phi because in my case the problem is simpler and the direction is invariant to orientation (so I have theta that goes from 0 to pi and phi that goes from 0 to pi):
    \nTheta x phi = 3,14 x 3,14 = 9,8596 -> total space
    \nUniform distribution of views:
    \n9,8596/1000 views = 0,009859 rad = 0,56° between two views.
    \nI think it is a reasonable error and it can be easily interpolated.
    \nIn your case, however, you can't reduce the range of phi (because rgb is not invariant to orientations), so you will have:
    \nTheta x phi = 3,14 x 6,28 = 19,719 -> total space
    \n19,719/1000 views = 0,0197 rad = 1,128° between two views.
    \nAn error of 1° is reasonable also in your case and if my calculations are exact, it could work even in your case but the error is the double of mine.

    \n
    \n

    Again, I would just caution whether we're following research that is attempting to capture shape in the embedding or whether the goal is capturing the shape just barely enough that color, i.e., a visual image can be constructed that \"looks good enough\". On quick read, that VINTER paper appears to be the latter, but I'd have to read it in more detail.

    ", + "id": 454678740, + "sender_full_name": "Sean", + "timestamp": 1722209117 + }, + { + "content": "

    As for total number of views, I think you could try as coarse as 45-degree increments. Resolution will need to be as fine as the smallest detail, which depends on the model size and detail complexity. I'd personally start at 1024x1024, about 1M per view.

    ", + "id": 454679031, + "sender_full_name": "Sean", + "timestamp": 1722209301 + }, + { + "content": "

    That 1024^2 resolution at 45-degree probably means something like 256 or 512 resolution alignment, whatever that cell size resolves to.

    ", + "id": 454679122, + "sender_full_name": "Sean", + "timestamp": 1722209386 } ] \ No newline at end of file diff --git a/zulip_json/111975-Google-Summer-of-Code/k-g.json b/zulip_json/111975-Google-Summer-of-Code/k-g.json index e63852cc4..29e9cb03a 100644 --- a/zulip_json/111975-Google-Summer-of-Code/k-g.json +++ b/zulip_json/111975-Google-Summer-of-Code/k-g.json @@ -1522,5 +1522,65 @@ "id": 454490305, "sender_full_name": "Louis Googl", "timestamp": 1722096627 + }, + { + "content": "

    Louis Googl said:

    \n
    \n

    regarding the ELEMENT_BEAM do we want to draw it as a line or we take the section and extrude it? in LS-PrePost they draw it as a line. the beams in the following image have an I section (section 1 in the second image) but they are represented as a line.
    \nimage.png
    \nimage.png

    \n
    \n

    For BRL-CAD, we need all elements as solids. I.e., they should have the real beam shape, if feasible. If that's too complicated or the real shape isn't well defined, a cuboid with corresponding line-of-sight value would be possible.

    ", + "id": 454622062, + "sender_full_name": "Daniel Rossberg", + "timestamp": 1722179322 + }, + { + "content": "

    Louis Googl said:

    \n
    \n

    another thing I am working on is the distinction between different commands and a command with options
    \nfor example *PART and *PART_ADAPTIVE_FAILURE are two different commands, but *PART and *PART_CONTACT are the same *PART command with the option CONTACT, also because the order of the options defines what the following cards (lines) contains, so I think we are going to need a vector of strings to hold the options and we need to combine the logic to check partLinesRead and the options vector to know what card we are reading. This will not be necessary for all the options as most of them aren't related to the geometry.
    \nimage.png
    \nimage.png

    \n
    \n

    In the converter, the distinction is done with the KState enumeration. This addresses the real different stuff. E.g., PART and PART_INERTIA are in fact the same, PART_ADAPTIVE_FAILURE is something different.

    \n

    Regarding the options, I'll review your proposal in the PR.

    ", + "id": 454623607, + "sender_full_name": "Daniel Rossberg", + "timestamp": 1722179950 + }, + { + "content": "

    Daniel Rossberg said:

    \n
    \n

    For BRL-CAD, we need all elements as solids. I.e., they should have the real beam shape, if feasible. If that's too complicated or the real shape isn't well defined, a cuboid with corresponding line-of-sight value would be possible.

    \n
    \n

    is there some example code I can look at?.
    \nfor a beam that have a T section we can devided to two arbs? or is there a methode for extrusion from the section?
    \nimage.png

    \n
    ", + "id": 454624416, + "sender_full_name": "Louis Googl", + "timestamp": 1722180326 + }, + { + "content": "

    Hmm, theoretically, we have the extrude primitive, which extrudes a 2D sketch along a line. Using arbs would be possible too (overlaps wouldn't matter).

    ", + "id": 454627291, + "sender_full_name": "Daniel Rossberg", + "timestamp": 1722181674 + }, + { + "content": "

    Regarding the options: Do we have a kind of stack there? E.g., for every option a global, section and element value? WE could think about crating an option object, which reacts on the enter section, enter element, exit element, and exit section events. (I don't know, if we can really say when we \"exit\" something, maybe we can see the \"enter\"s only.)

    ", + "id": 454628522, + "sender_full_name": "Daniel Rossberg", + "timestamp": 1722182131 + }, + { + "content": "

    the option card or line follows directly under the command say we have Element_Beam_Section card3 will be there and card2 wouldn't. this is the way to know that you are exiting an element, number of lines is equal to the required card pluse the number of options.
    \nimage.png

    \n
    ", + "id": 454633071, + "sender_full_name": "Louis Googl", + "timestamp": 1722183898 + }, + { + "content": "

    Louis Googl said:

    \n
    \n

    another issue is that an option can override properties that are defined in other places, for example if we have *SECTION_BEAM with a certain thickness and then *ELEMENT_BEAM with the option THICKNESS i.e *ELEMENT_BEAM_THICKNESS the latter command overrides the thickness defined in the former command. I am trying to find a way to protect the information provided by the superiour command so it doesn't change if the *SECTION_BEAM comes after *ELEMENT_BEAM_THICKNESS

    \n
    \n

    I understood that *SECTION_BEAM and *ELEMENT_BEAM can be in any order anywhere in the data. The link will be created when parsing KData via the IDs. I.e., the thicknesses have to be stored in the data and evaluated when parsing it.

    ", + "id": 454643073, + "sender_full_name": "Daniel Rossberg", + "timestamp": 1722187754 + }, + { + "content": "

    yes, now I understand what you meant. exactly the the ID is the link. but after storing the thickness from ELMENT_BEAM_THICKNESS it needs to be protected from change, and if it's taken from SECTION_BEAM it should be modifiable.

    ", + "id": 454645395, + "sender_full_name": "Louis Googl", + "timestamp": 1722189576 + }, + { + "content": "

    You need a kind of std::map<std::string, std::string> or std::map<std::string, double> for the options:

    \n
    thickness := default\n\nif SECTION.options[THICKNESS] then\n    thickness := SECTION.options[THICKNESS]\n\nif ELEMENT.options[THICKNESS] then\n    thickness := ELEMENT.options[THICKNESS]\n
    \n

    (That's pseudo-code, of course.)

    ", + "id": 454650136, + "sender_full_name": "Daniel Rossberg", + "timestamp": 1722192102 + }, + { + "content": "

    Examples of sketch+extrude are at https://brlcad.org/wiki/Sketch and https://brlcad.org/wiki/Extrude

    ", + "id": 454676974, + "sender_full_name": "Sean", + "timestamp": 1722207783 } ] \ No newline at end of file diff --git a/zulip_json/stream_index.json b/zulip_json/stream_index.json index f2e6d7fa4..f74194efc 100644 --- a/zulip_json/stream_index.json +++ b/zulip_json/stream_index.json @@ -420,7 +420,7 @@ }, "Google Summer of Code": { "id": 111975, - "latest_id": 454545008, + "latest_id": 454679122, "topic_data": { "(no topic)": { "latest_date": 1711223600, @@ -515,8 +515,8 @@ "size": 2 }, "Neural Rendering": { - "latest_date": 1722125811, - "size": 277 + "latest_date": 1722209386, + "size": 291 }, "New BRL-CAD GUI": { "latest_date": 1720457169, @@ -611,8 +611,8 @@ "size": 22 }, "k-g": { - "latest_date": 1722096627, - "size": 254 + "latest_date": 1722207783, + "size": 264 }, "manifold": { "latest_date": 1720668301, @@ -654,7 +654,7 @@ }, "announce": { "id": 104061, - "latest_id": 454539140, + "latest_id": 454681537, "topic_data": { "(no topic)": { "latest_date": 1513142435, @@ -741,8 +741,8 @@ "size": 2 }, "signups": { - "latest_date": 1722121881, - "size": 3830 + "latest_date": 1722211144, + "size": 3833 }, "welcome": { "latest_date": 1508904227, @@ -752,7 +752,7 @@ }, "brlcad": { "id": 104062, - "latest_id": 454537799, + "latest_id": 454681576, "topic_data": { "#brlcad": { "latest_date": 1577725729, @@ -1299,8 +1299,8 @@ "size": 59 }, "build": { - "latest_date": 1722051115, - "size": 915 + "latest_date": 1722211171, + "size": 916 }, "build performance": { "latest_date": 1656420374, @@ -1367,8 +1367,8 @@ "size": 2 }, "conversion": { - "latest_date": 1721127931, - "size": 5 + "latest_date": 1722211056, + "size": 6 }, "cool geometry": { "latest_date": 1595456600, @@ -2159,5 +2159,5 @@ } } }, - "time": 1722127420.366223 + "time": 1722213687.178732 } \ No newline at end of file