Papers › Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point...

Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis

14 Mar 2023arXiv:2303.08134archive 2025-07-28

Renrui Zhang, Liuhui Wang, Ziyu Guo, Yali Wang, Peng Gao, Hongsheng Li, Jianbo Shi

We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions. Surprisingly, it performs well on various 3D tasks, requiring no parameters or training, and even surpasses existing fully trained models. Starting from this basic non-parametric model, we propose two extensions. First, Point-NN can serve as a base architectural framework to construct Parametric Networks by simply inserting linear layers on top. Given the superior non-parametric foundation, the derived Point-PN exhibits a high performance-efficiency trade-off with only a few learnable parameters. Second, Point-NN can be regarded as a plug-and-play module for the already trained 3D models during inference. Point-NN captures the complementary geometric knowledge and enhances existing methods for different 3D benchmarks without re-training. We hope our work may cast a light on the community for understanding 3D point clouds with non-parametric methods. Code is available at https://github.com/ZrrSkywalker/Point-NN.

PaperPDFCodeCode 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="2303.08134")

Code

Syntology Ran 1 of 2 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · our draft was wrong.

By repository: community (archive-listed): 2 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

zrrskywalker/point-nn mentioned in papermentioned on GitHubpytorch report
asalarpour/Point_GN mentioned on GitHubpytorch report
opengvlab/cafo mentioned on GitHubpytorch 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

2 samples harvested; 1 ran; 0 honoured the contract we drafted; 1 has 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 · our draft was wrong
1unverified

Licence: 2 of the 2 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 asalarpour/Point_GN. “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.

process_data asalarpour/Point_GN/train_free_main.py community (archive-listed) ran · our draft was wrong no licence file found · pointer only · d035ce15cf041e94 · report
normalize_tensor asalarpour/Point_GN/models/point_gn.py community (archive-listed) unverified no licence file found · pointer only · fd36ede5044ec409 · report

Tasks

3D Point Cloud ClassificationAllSupervised Only 3D Point Cloud ClassificationTraining-free 3D Part SegmentationTraining-free 3D Point Cloud Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Point Cloud Classification ModelNet40 Point-PN Number of params 0.8M #47 of 111 Archive leaderboard report
3D Point Cloud Classification ModelNet40 Point-PN Overall Accuracy 93.8 #47 of 111 Archive leaderboard report
3D Point Cloud Classification ScanObjectNN Point-PN Number of params 0.8M #45 of 77 Archive leaderboard report
3D Point Cloud Classification ScanObjectNN Point-PN Overall Accuracy 87.1 #45 of 77 Archive leaderboard report
Supervised Only 3D Point Cloud Classification ScanObjectNN Point-PN Number of params (M) 0.8 #7 of 12 Archive leaderboard report
Supervised Only 3D Point Cloud Classification ScanObjectNN Point-PN Overall Accuracy (PB_T50_RS) 87.1 #7 of 12 Archive leaderboard report
Training-free 3D Part Segmentation ShapeNet-Part Point-NN Need 3D Data? Yes #1 of 3 Archive leaderboard report
Training-free 3D Part Segmentation ShapeNet-Part Point-NN Parameters 0M #1 of 3 Archive leaderboard report
Training-free 3D Part Segmentation ShapeNet-Part Point-NN mIoU 74.0 #1 of 3 Archive leaderboard report
Training-free 3D Point Cloud Classification ModelNet40 Point-NN Accuracy (%) 82.6 #2 of 7 Archive leaderboard report
Training-free 3D Point Cloud Classification ModelNet40 Point-NN Need 3D Data? Yes #2 of 7 Archive leaderboard report
Training-free 3D Point Cloud Classification ModelNet40 Point-NN Parameters 0M #2 of 7 Archive leaderboard report
Training-free 3D Point Cloud Classification ScanObjectNN Point-NN Accuracy (%) 64.9 #2 of 6 Archive leaderboard report
Training-free 3D Point Cloud Classification ScanObjectNN Point-NN Need 3D Data? Yes #2 of 6 Archive leaderboard report
Training-free 3D Point Cloud Classification ScanObjectNN Point-NN Parameters 0M #2 of 6 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

BASE

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