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 181–190 of 272 papers

TitleStatusHype
Projection-based Point Convolution for Efficient Point Cloud SegmentationCode0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation—0
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph PropagationCode0
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation—0
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving—0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Point Cloud Segmentation Using Sparse Temporal Local Attention—0
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation—0
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation—0
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation—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