Papers › TextureNet: Consistent Local Parametrizations for Learning from High-Resolution...

TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes

30 Nov 2018CVPR 2019 6arXiv:1812.00020archive 2025-07-28

Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Nießner, Leonidas Guibas

We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e.g., color texture maps). The key idea is to utilize a 4-rotational symmetric (4-RoSy) field to define a domain for convolution on a surface. Though 4-RoSy fields have several properties favorable for convolution on surfaces (low distortion, few singularities, consistent parameterization, etc.), orientations are ambiguous up to 4-fold rotation at any sample point. So, we introduce a new convolutional operator invariant to the 4-RoSy ambiguity and use it in a network to extract features from high-resolution signals on geodesic neighborhoods of a surface. In comparison to alternatives, such as PointNet based methods which lack a notion of orientation, the coherent structure given by these neighborhoods results in significantly stronger features. As an example application, we demonstrate the benefits of our architecture for 3D semantic segmentation of textured 3D meshes. The results show that our method outperforms all existing methods on the basis of mean IoU by a significant margin in both geometry-only (6.4%) and RGB+Geometry (6.9-8.2%) settings.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1812.00020")

Code

Syntology Ran 0 of 11 code samples harvested from 1 repository linked to this paper; 11 have no recorded run.

By repository: community (archive-listed): 11 samples from 1 repository, 0 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

hjwdzh/TextureNet mentioned on GitHubMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

11 samples harvested; 0 ran; 0 honoured the contract we drafted; 11 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

11unverified

Licence: 0 of the 11 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from hjwdzh/TextureNet. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “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.

Each sample ends with its 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.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at 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 label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

Estimate hjwdzh/TextureNet/src/execute.py community (archive-listed) unverified MIT (permissive) · 601a0819ff828e20 · report
Evaluate hjwdzh/TextureNet/src/execute.py community (archive-listed) unverified MIT (permissive) · 2aaefcdb61ef72e7 · report
LoadChunk hjwdzh/TextureNet/src/dataset/dataset.py community (archive-listed) unverified MIT (permissive) · c6c6226461592092 · report
Preprocess hjwdzh/TextureNet/src/dataset/dataset.py community (archive-listed) unverified MIT (permissive) · c38d59010770b5bb · report
conv1d hjwdzh/TextureNet/src/models/tf_util.py community (archive-listed) unverified MIT (permissive) · 18d51d8d06002af3 · report
conv2d hjwdzh/TextureNet/src/models/tf_util.py community (archive-listed) unverified MIT (permissive) · dd4ea839e0207aea · report
conv2d_transpose hjwdzh/TextureNet/src/models/tf_util.py community (archive-listed) unverified MIT (permissive) · 5d5a54e9e61f622c · report
get_iou hjwdzh/TextureNet/src/execute.py community (archive-listed) unverified MIT (permissive) · 2d1cba39d92e0613 · report
get_iou hjwdzh/TextureNet/evaluate/evaluate_confusion.py community (archive-listed) unverified MIT (permissive) · 1c60fe5a3acb8f46 · report
get_loss hjwdzh/TextureNet/src/models/texturenet.py community (archive-listed) unverified MIT (permissive) · 3780aaaf4cf88d7f · report
placeholder_inputs hjwdzh/TextureNet/src/models/texturenet.py community (archive-listed) unverified MIT (permissive) · d7c375f4f920af84 · report

Tasks

3D Semantic SegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Segmentation ScanNet TextureNet test mIoU 56.6 #37 of 45 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Convolution

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