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 110 of 272 papers

TitleStatusHype
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial IntelligenceCode4
Generalizable Humanoid Manipulation with 3D Diffusion PoliciesCode4
Generalized Robot 3D Vision-Language Model with Fast Rendering and Pre-Training Vision-Language AlignmentCode3
OctFormer: Octree-based Transformers for 3D Point CloudsCode2
Masked Autoencoders for Point Cloud Self-supervised LearningCode2
Multimodality Helps Few-Shot 3D Point Cloud Semantic SegmentationCode2
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language ModelCode2
FEC: Fast Euclidean Clustering for Point Cloud SegmentationCode2
DINO in the Room: Leveraging 2D Foundation Models for 3D SegmentationCode2
OneFormer3D: One Transformer for Unified Point Cloud SegmentationCode2
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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