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load_K_Rt_from_P

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

load_K_Rt_from_P appears in the code Syntology harvested for 21 papers, as 6 distinct code bodies found in 24 places (a place is one code body under one paper). At least one of them ran in 11 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 load_K_Rt_from_P 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 6 distinct code bodies named load_K_Rt_from_P; 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
5ran
1unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 3 of the 24 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

21 papers shown of 21, newest first; 24 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; 2 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
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $α$-NeuS 8 Nov 2024 728388808/alpha-NeuS/models/dataset.py 69986a1a9ebdac78 unverified no licence file found · pointer only
Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction 5 Sep 2024 prstrive/SuRF/datasets/dtu_finetune.py c6324b632487a725 ran MIT (permissive)
Surface-Centric Modeling for High-Fidelity Generalizable Neural Surface Reconstruction 5 Sep 2024 prstrive/SuRF/evaluation/clean_mesh.py 7a8bf46877ee62d3 ran MIT (permissive)
SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization 19 Jul 2024 maeyounes/SparseCraft/datasets/sparsecraft_utils.py aedca8b114d0c45b ran MIT (permissive)
GenS: Generalizable Neural Surface Reconstruction from Multi-View Images 4 Jun 2024 prstrive/gens/datasets/bmvs_finetune.py c6324b632487a725 ran MIT (permissive)
GenS: Generalizable Neural Surface Reconstruction from Multi-View Images 4 Jun 2024 prstrive/gens/evaluation/clean_meshes.py 7a8bf46877ee62d3 ran MIT (permissive)
QuasiSim: Parameterized Quasi-Physical Simulators for Dexterous Manipulations Transfer 11 Apr 2024 meowuu7/quasisim/models/dataset.py 69986a1a9ebdac78 unverified MIT (permissive)
Omni-Recon: Harnessing Image-based Rendering for General-Purpose Neural Radiance Fields 17 Mar 2024 GATECH-EIC/Omni-Recon/evaluation/clean_mesh.py 7a8bf46877ee62d3 ran MIT (permissive)
UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and UnFavOrable Sets 8 Mar 2024 Youngju-Na/UFORecon/evaluation/clean_mesh.py 7a8bf46877ee62d3 ran MIT (permissive)
Reducing Shape-Radiance Ambiguity in Radiance Fields with a Closed-Form Color Estimation Method 20 Dec 2023 qihangGH/Closed-form-color-field/opt/util/dtu_dataset.py c6324b632487a725 ran MIT (permissive)
Learning Naturally Aggregated Appearance for Efficient 3D Editing 11 Dec 2023 felixcheng97/agap/lib/load_dtu.py d250a3f916e450fd ran GPL-3.0 (copyleft) · pointer only
SuperNormal: Neural Surface Reconstruction via Multi-View Normal Integration 8 Dec 2023 CyberAgentAILab/SuperNormal/models/dataset_loader.py 41d490f153497337 ran MIT (permissive)
Coordinate Quantized Neural Implicit Representations for Multi-view Reconstruction 21 Aug 2023 machineperceptionlab/cq-nir/neus_cq/models/dataset.py 69986a1a9ebdac78 unverified no licence file found · pointer only
Looking Through the Glass: Neural Surface Reconstruction Against High Specular Reflections 18 Apr 2023 jiaxiongq/neus-hsr/models/dataset.py 69986a1a9ebdac78 unverified MIT (permissive)
VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization 31 Mar 2023 BoifZ/VDN-NeRF/dpt_models/dataset.py 69986a1a9ebdac78 unverified MIT (permissive)
NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from Multi-view Images 21 Mar 2023 xmeng525/NeAT/models/dataset.py 69986a1a9ebdac78 unverified MIT (permissive)
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors 27 Jun 2022 jiepengwang/NeuRIS/models/dataset.py 69986a1a9ebdac78 unverified MIT (permissive)
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction 31 May 2022 ghixu/geo-neus/models/dataset.py 69986a1a9ebdac78 unverified MIT (permissive)
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding 16 Jan 2022 Jittor/JNeRF/python/jnerf/dataset/neus_dataset.py 69986a1a9ebdac78 unverified Apache-2.0 (permissive)
Volume Rendering of Neural Implicit Surfaces 22 Jun 2021 lioryariv/volsdf/code/utils/rend_util.py d250a3f916e450fd ran MIT (permissive)
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction 20 Jun 2021 Totoro97/NeuS/models/dataset.py 69986a1a9ebdac78 unverified MIT (permissive)
arXiv:aaai_27938 qihangGH/IMRC/DirectVoxGO/lib/load_dtu.py d250a3f916e450fd ran MIT (permissive)
arXiv:Ren_VolRecon_Volume_Rendering_of_Signed_Ray_Distance_Functions_for_Generalizable_CVPR_2023_paper IVRL/VolRecon/evaluation/clean_mesh.py 7a8bf46877ee62d3 ran MIT (permissive)
arXiv:Ren_VolRecon_Volume_Rendering_of_Signed_Ray_Distance_Functions_for_Generalizable_CVPR_2023_paper IVRL/VolRecon/code/dataset/dtu_test_sparse.py 69986a1a9ebdac78 unverified MIT (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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