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

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Showing 141–150 of 272 papers

TitleStatusHype
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance FieldsCode0
U3DS^3: Unsupervised 3D Semantic Scene Segmentation—0
Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation—0
2D-3D Interlaced Transformer for Point Cloud Segmentation with Scene-Level Supervision—0
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation—0
Towards Robust Few-shot Point Cloud Semantic SegmentationCode0
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid—0
pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud SegmentationCode0
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data—0
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Benchmark Results

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