SOTAVerified

Point Cloud Segmentation

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation.

Source: 3D point cloud segmentation: A survey

Papers

Showing 8190 of 272 papers

TitleStatusHype
OneFormer3D: One Transformer for Unified Point Cloud SegmentationCode2
DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance FieldsCode0
U3DS^3: Unsupervised 3D Semantic Scene Segmentation0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation0
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision0
PointHR: Exploring High-Resolution Architectures for 3D Point Cloud SegmentationCode1
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation0
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
Towards Robust Few-shot Point Cloud Semantic SegmentationCode0
Compositional Semantic Mix for Domain Adaptation in Point Cloud SegmentationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13Unverified
3PointNet++mean Corruption Error (mCE)1.11Unverified
4PointTransformersmean Corruption Error (mCE)1.05Unverified
5PointMLPmean Corruption Error (mCE)0.98Unverified
6PointMAEmean Corruption Error (mCE)0.93Unverified
7GDANetmean Corruption Error (mCE)0.92Unverified
8GDANetmean Corruption Error (mCE)0.89Unverified