Papers › Equivariant Point Cloud Analysis via Learning Orientations for Message Passing

Equivariant Point Cloud Analysis via Learning Orientations for Message Passing

28 Mar 2022CVPR 2022 1arXiv:2203.14486archive 2025-07-28

Shitong Luo, Jiahan Li, Jiaqi Guan, Yufeng Su, Chaoran Cheng, Jian Peng, Jianzhu Ma

Equivariance has been a long-standing concern in various fields ranging from computer vision to physical modeling. Most previous methods struggle with generality, simplicity, and expressiveness -- some are designed ad hoc for specific data types, some are too complex to be accessible, and some sacrifice flexible transformations. In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme. We find the equivariant property could be obtained by introducing an orientation for each point to decouple the relative position for each point from the global pose of the entire point cloud. Therefore, we extend current message passing networks with a module that learns orientations for each point. Before aggregating information from the neighbors of a point, the networks transforms the neighbors' coordinates based on the point's learned orientations. We provide formal proofs to show the equivariance of the proposed framework. Empirically, we demonstrate that our proposed method is competitive on both point cloud analysis and physical modeling tasks. Code is available at https://github.com/luost26/Equivariant-OrientedMP .

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="2203.14486")

Code

Syntology Ran 6 of 15 code samples harvested from 1 repository linked to this paper; 9 have no recorded run. Of those that ran: 1 ran · honoured contract; 4 ran · our draft was wrong; 1 ran · fixture could not drive it.

By repository: official repository: 15 samples from 1 repository, 6 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

luost26/equivariant-orientedmp officialmentioned in papermentioned on GitHubpytorchMIT 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

15 samples harvested; 6 ran; 1 honoured the contract we drafted; 9 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.

1ran · honoured contract
4ran · our draft was wrong
1ran · fixture could not drive it
9unverified

Licence: 0 of the 15 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 luost26/equivariant-orientedmp. “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.

knn luost26/equivariant-orientedmp/models/cls/oriented_dgcnn.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · cdd0141594039dcb · report
normalize_vector luost26/equivariant-orientedmp/modules/geometric.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · d26ff18f89e81ea5 · report
pc_normalize luost26/equivariant-orientedmp/datasets/modelnet.py official repository ran · honoured contract fingerprinted MIT (permissive) · 4783fbece52f500e · report
project_v2v luost26/equivariant-orientedmp/modules/geometric.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · e83f79b6a566bc97 · report
register_model luost26/equivariant-orientedmp/models/cls/_registry.py official repository ran · our draft was wrong MIT (permissive) · be3d8f2945b52b90 · report
tuple_index luost26/equivariant-orientedmp/modules/gvp.py official repository ran · our draft was wrong MIT (permissive) · 38735f4bca7f8220 · report
calculate_shape_IoU luost26/equivariant-orientedmp/train_partseg.py official repository unverified MIT (permissive) · 73a932f7826aa350 · report
farthest_point_sample luost26/equivariant-orientedmp/datasets/modelnet.py official repository unverified MIT (permissive) · f80066a00e7156a2 · report
gather luost26/equivariant-orientedmp/models/cls/oriented_dgcnn.py official repository unverified MIT (permissive) · 8e1b540b7dbb4fd7 · report
get_graph_feature luost26/equivariant-orientedmp/models/cls/oriented_dgcnn.py official repository unverified MIT (permissive) · d98fb5121c2ad8dd · report
get_hierarchical_idx luost26/equivariant-orientedmp/modules/frame.py official repository unverified MIT (permissive) · 9bad2eb9e3f5facf · report
get_hierarchical_idx luost26/equivariant-orientedmp/models/cls/oriented_rscnn.py official repository unverified MIT (permissive) · cc0632008d5a9aef · report
get_model luost26/equivariant-orientedmp/models/cls/_registry.py official repository unverified MIT (permissive) · be1d33da9ab60900 · report
randn luost26/equivariant-orientedmp/modules/gvp.py official repository unverified MIT (permissive) · 28e463a04d36b65d · report
safe_norm luost26/equivariant-orientedmp/modules/geometric.py official repository unverified MIT (permissive) · e253039723a2f1f8 · report

Tasks

Graph Neural Network

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

HOC

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