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 4150 of 272 papers

TitleStatusHype
Lidar Panoptic Segmentation in an Open WorldCode1
Efficiently Expanding Receptive Fields: Local Split Attention and Parallel Aggregation for Enhanced Large-scale Point Cloud Semantic Segmentation0
When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse LabelsCode0
Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution AlignmentCode1
GSTran: Joint Geometric and Semantic Coherence for Point Cloud SegmentationCode0
Trainable Pointwise Decoder Module for Point Cloud Segmentation0
Fine-grained Metrics for Point Cloud Semantic Segmentation0
Scale Disparity of Instances in Interactive Point Cloud Segmentation0
SegPoint: Segment Any Point Cloud via Large Language Model0
Dual-level Adaptive Self-Labeling for Novel Class Discovery 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