paper-with-me

Supervised Only 3D Point Cloud Classification 벤치마크

Supervised Only 3D Point Cloud Classification on ScanObjectNN

36개 결과 · ⬇ CSV · JSON

Overall Accuracy (PB_T50_RS)

68 74.16 80.32 86.48 92.64 2016-12 2026-11 PointNet — 68.0 (2016-12-02) PointNet — 68.0 (2016-12-02) PointNet — 68.0 (2016-12-02) PointNet++ — 77.9 (2017-06-07) PointNet++ — 77.9 (2017-06-07) PointNet++ — 77.9 (2017-06-07) Transformer — 77.24 (2017-06-12) Transformer — 77.24 (2017-06-12) Transformer — 77.24 (2017-06-12) DGCNN — 78.1 (2018-01-24) DGCNN — 78.1 (2018-01-24) DGCNN — 78.1 (2018-01-24) PointMLP — 85.4 (2022-02-15) PointMLP — 85.4 (2022-02-15) PointMLP — 85.4 (2022-02-15) PointNeXt — 87.8 (2022-06-09) PointNeXt — 87.8 (2022-06-09) PointNeXt — 87.8 (2022-06-09) Point-PN — 87.1 (2023-03-14) Point-PN — 87.1 (2023-03-14) Point-PN — 87.1 (2023-03-14) SPoTr — 88.6 (2023-03-29) SPoTr — 88.6 (2023-03-29) SPoTr — 88.6 (2023-03-29) DeLA — 90.4 (2023-08-31) DeLA — 90.4 (2023-08-31) DeLA — 90.4 (2023-08-31) PCM — 88.1 (2024-03-01) PCM — 88.1 (2024-03-01) PCM — 88.1 (2024-03-01) Mamba3D — 92.64 (2024-04-23) Mamba3D (no voting) — 91.81 (2024-04-23) Mamba3D — 92.64 (2024-04-23) Mamba3D (no voting) — 91.81 (2024-04-23) Mamba3D — 92.64 (2024-04-23) Mamba3D (no voting) — 91.81 (2024-04-23) PointNet — 68.0 (2016-12-02) PointNet++ — 77.9 (2017-06-07) DGCNN — 78.1 (2018-01-24) PointMLP — 85.4 (2022-02-15) PointNeXt — 87.8 (2022-06-09) SPoTr — 88.6 (2023-03-29) DeLA — 90.4 (2023-08-31) Mamba3D — 92.64 (2024-04-23)
RankModel Overall Accuracy (PB_T50_RS)GFLOPsNumber of params (M) PaperCodeYear
1 Mamba3D 92.643.916.9 Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model xhanxu/Mamba3D 2024
2 Mamba3D (no voting) 91.813.916.9 Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model xhanxu/Mamba3D 2024
3 DeLA 90.41.55.3 Decoupled Local Aggregation for Point Cloud Learning matrix-asc/dela 2023
4 SPoTr 88.610.81.7 Self-positioning Point-based Transformer for Point Cloud Understanding mlvlab/spotr 2023
5 PCM 88.145.034.2 Point Cloud Mamba: Point Cloud Learning via State Space Model skyworkai/pointcloudmamba · zhang-tao-whu/pcm 2024
6 PointNeXt 87.83.61.4 PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies guochengqian/pointnext · linhaojia13/pointmetabase · boyden/pointtransformerfl 2022
7 Point-PN 87.1–0.8 Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis zrrskywalker/point-nn · opengvlab/cafo · asalarpour/Point_GN 2023
8 PointMLP 85.431.412.6 Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework ma-xu/pointmlp-pytorch 2022
9 DGCNN 78.12.41.8 Dynamic Graph CNN for Learning on Point Clouds WangYueFt/dgcnn · vinits5/learning3d · AnTao97/dgcnn.pytorch · +18 2018
10 PointNet++ 77.91.71.5 PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space yanx27/Pointnet_Pointnet2_pytorch · charlesq34/pointnet2 · facebookresearch/votenet · +65 2017
11 Transformer 77.244.822.1 Attention Is All You Need huggingface/transformers · labmlai/annotated_deep_learning_paper_implementations · facebookresearch/fairseq · +592 2017
12 PointNet 68.00.53.5 PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation charlesq34/pointnet · fxia22/pointnet.pytorch · WangYueFt/dgcnn · +107 2016
13 Mamba3D 92.643.916.9 Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model xhanxu/Mamba3D 2024
14 Mamba3D (no voting) 91.813.916.9 Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model xhanxu/Mamba3D 2024
15 DeLA 90.41.55.3 Decoupled Local Aggregation for Point Cloud Learning matrix-asc/dela 2023
16 SPoTr 88.610.81.7 Self-positioning Point-based Transformer for Point Cloud Understanding mlvlab/spotr 2023
17 PCM 88.145.034.2 Point Cloud Mamba: Point Cloud Learning via State Space Model skyworkai/pointcloudmamba · zhang-tao-whu/pcm 2024
18 PointNeXt 87.83.61.4 PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies guochengqian/pointnext · linhaojia13/pointmetabase · boyden/pointtransformerfl 2022
19 Point-PN 87.1–0.8 Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis zrrskywalker/point-nn · opengvlab/cafo · asalarpour/Point_GN 2023
20 PointMLP 85.431.412.6 Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework ma-xu/pointmlp-pytorch 2022
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