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convbn_3d

Syntologyentry name in harvested coderead from the graph 2026-09-24

convbn_3d appears in the code Syntology harvested for 20 papers, as 7 distinct code bodies found in 23 places (a place is one code body under one paper). At least one of them ran in 18 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named convbn_3d do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 5 of the 7 distinct code bodies named convbn_3d; 2 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
1ran · our draft was wrong
0ran · fixture could not drive it
4ran
2unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 23 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

20 papers shown of 20, newest first; 23 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive; 3 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Self-Distilled Depth Refinement with Noisy Poisson Fusion 26 Sep 2024 lijia7/sddr/utils/submodule.py 252c55c642e6a443 ran no licence file found · pointer only
Temporal Event Stereo via Joint Learning with Stereoscopic Flow 15 Jul 2024 mickeykang16/temporaleventstereo/models/submodule.py 46674adf87c5459d ran no licence file found · pointer only
OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments 14 Dec 2023 linshan-bin/occnerf/networks/_3DCNN.py ea83495b07c4dc60 ran Apache-2.0 (permissive)
OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments 14 Dec 2023 linshan-bin/occnerf/networks/submodule.py 252c55c642e6a443 ran Apache-2.0 (permissive)
MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection 18 Aug 2023 cskkxjk/MonoNeRD/mononerd/models/backbones_3d_mono/submodule.py 480bf7306505e398 ran MIT (permissive)
SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving 16 Mar 2023 ganwanshui/simpleoccupancy/networks/occupancy_decoder.py ea83495b07c4dc60 ran no licence file found · pointer only
Stereo Neural Vernier Caliper 21 Mar 2022 Nicholasli1995/SNVC/snvc/models/vernier.py 7a0e35eeadc577b2 ran · our draft was wrong MIT (permissive)
LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector 18 Aug 2021 xy-guo/LIGA-Stereo/liga/models/backbones_3d_stereo/submodule.py 480bf7306505e398 ran Apache-2.0 (permissive)
SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching 25 May 2021 hongzhidu/SRHNet/models/SHRNet.py 46674adf87c5459d ran MIT (permissive)
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching 9 Apr 2021 gallenszl/MSMD-Net/models/submodule.py 252c55c642e6a443 ran MIT (permissive)
AANet: Adaptive Aggregation Network for Efficient Stereo Matching 20 Apr 2020 haofeixu/aanet/nets/aggregation.py 46674adf87c5459d ran Apache-2.0 (permissive)
DeepSFM: Structure From Motion Via Deep Bundle Adjustment 20 Dec 2019 weixk2015/DeepSFM/models/submodule.py c266e9e34b51eb96 unverified BSD-3-Clause (permissive)
Normal Assisted Stereo Depth Estimation 24 Nov 2019 udaykusupati/Normal-Assisted-Stereo/models/submodule.py 46674adf87c5459d ran MIT (permissive)
360SD-Net: 360° Stereo Depth Estimation with Learnable Cost Volume 11 Nov 2019 albert100121/360SD-Net/models/sub_ASPP.py 46674adf87c5459d ran MIT (permissive)
DPSNet: End-to-end Deep Plane Sweep Stereo 2 May 2019 sunghoonim/DPSNet/models/submodule.py 46674adf87c5459d ran MIT (permissive)
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction 24 Jul 2018 meteorshowers/StereoNet/disparity/models/stereonet_disp.py 873f10dc18effe9c unverified MIT (permissive)
Digging Into Self-Supervised Monocular Depth Estimation 4 Jun 2018 qrzyang/pseudo-stereo/models/psm_submodule.py 46674adf87c5459d ran Apache-2.0 (permissive)
Pyramid Stereo Matching Network 23 Mar 2018 JiaRenChang/PSMNet/models/submodule.py 46674adf87c5459d ran MIT (permissive)
arXiv:aaai_20056 SpadeLiu/Lac-GwcNet/networks/submodule.py 46674adf87c5459d ran MIT (permissive)
arXiv:Xu_Adaptive_Multi-Modal_Cross-Entropy_Loss_for_Stereo_Matching_CVPR_2024_paper xxxupeng/ADL/backbones/GwcNet/submodule.py 252c55c642e6a443 ran MIT (permissive)
arXiv:Xu_Adaptive_Multi-Modal_Cross-Entropy_Loss_for_Stereo_Matching_CVPR_2024_paper xxxupeng/ADL/backbones/PSMNet/submodule.py 46674adf87c5459d ran MIT (permissive)
arXiv:Gan_GaussianOcc_Fully_Self-supervised_and_Efficient_3D_Occupancy_Estimation_with_Gaussian_ICCV_2025_paper GANWANSHUI/GaussianOcc/networks/_3DCNN.py ea83495b07c4dc60 ran Apache-2.0 (permissive)
arXiv:Gan_GaussianOcc_Fully_Self-supervised_and_Efficient_3D_Occupancy_Estimation_with_Gaussian_ICCV_2025_paper GANWANSHUI/GaussianOcc/networks/submodule.py 252c55c642e6a443 ran Apache-2.0 (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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