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 6170 of 272 papers

TitleStatusHype
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point CloudCode1
Point TransformerCode1
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene CompletionCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
Unsupervised Point Cloud Pre-Training via Occlusion CompletionCode1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
KPRNet: Improving projection-based LiDAR semantic segmentationCode1
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer LabelsCode1
Dense-Resolution Network for Point Cloud Classification and 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