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
SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation—0
Scale Disparity of Instances in Interactive Point Cloud Segmentation—0
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds—0
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data—0
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel—0
Language-Level Semantics Conditioned 3D Point Cloud Segmentation—0
SegPoint: Segment Any Point Cloud via Large Language Model—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance—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