Home › Code › transformation_from_parameters

transformation_from_parameters

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

transformation_from_parameters appears in the code Syntology harvested for 18 papers, as 4 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 17 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 transformation_from_parameters 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 3 of the 4 distinct code bodies named transformation_from_parameters; 1 is unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
3ran
1unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 4 of the 20 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

18 papers shown of 18, newest first; 20 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
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth Estimation 19 Jul 2024 liujf1226/mono-vifi/layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
SPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation 18 Apr 2024 Lavreniuk/SPIdepth/calc_layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving 26 Mar 2024 Gandolfczjh/3D2Fool/layers.py cdc03d6bfc4d3a34 ran no licence file found · pointer only
OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments 14 Dec 2023 linshan-bin/occnerf/utils/layers.py cdc03d6bfc4d3a34 ran Apache-2.0 (permissive)
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions 23 Oct 2023 SenZHANG-GitHub/ekf-imu-depth/layers.py cdc03d6bfc4d3a34 ran no licence file found · pointer only
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions 23 Oct 2023 brandleyzhou/DIFFNet/layers.py de769323d28db252 ran no licence file found · pointer only
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions 23 Oct 2023 hyBlue/FSRE-Depth/utils/depth_utils.py c61a8df97cd03469 ran MIT (permissive)
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes 26 Sep 2023 zxcqlf/GasMono/layers.py cdc03d6bfc4d3a34 ran no licence file found · pointer only
Towards Better Data Exploitation in Self-Supervised Monocular Depth Estimation 11 Sep 2023 LiuJF1226/BDEdepth/layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
Policy Pre-training for Autonomous Driving via Self-supervised Geometric Modeling 3 Jan 2023 opendrivelab/ppgeo/layers.py cdc03d6bfc4d3a34 ran Apache-2.0 (permissive)
SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimation 7 Apr 2022 weiyithu/surrounddepth/layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
AP-BSN: Self-Supervised Denoising for Real-World Images via Asymmetric PD and Blind-Spot Network 22 Mar 2022 ucaszyp/steps/components/layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR 20 Sep 2021 AutoAILab/FusionDepth/layers.py 03d0f03d4f4d3260 unverified MIT (permissive)
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark 9 Aug 2021 w2kun/RNW/components/layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
Learning Stereo from Single Images 4 Aug 2020 mattpoggi/depthstillation/geometry.py cdc03d6bfc4d3a34 ran MIT (permissive)
From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation 24 Jul 2019 ku-cvlab/maskingdepth/layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
Unity: A General Platform for Intelligent Agents 7 Sep 2018 vkakerbeck/ml-agents-dev/ml-agents-dev/mlagentsdev/trainers/depth_layers.py cdc03d6bfc4d3a34 ran Apache-2.0 (permissive)
arXiv:aaai_28383 hisfog/SfMNeXt-Impl/calc_layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
arXiv:Zhou_R-MSFM_Recurrent_Multi-Scale_Feature_Modulation_for_Monocular_Depth_Estimating_ICCV_2021_paper jsczzzk/R-MSFM/layers.py cdc03d6bfc4d3a34 ran MIT (permissive)
arXiv:Gan_GaussianOcc_Fully_Self-supervised_and_Efficient_3D_Occupancy_Estimation_with_Gaussian_ICCV_2025_paper GANWANSHUI/GaussianOcc/utils/layers.py cdc03d6bfc4d3a34 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".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections