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 131–140 of 272 papers

TitleStatusHype
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation—0
Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation—0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
Construct to Associate: Cooperative Context Learning for Domain Adaptive Point Cloud Segmentation—0
MirageRoom: 3D Scene Segmentation with 2D Pre-trained Models by Mirage Projection—0
Multi-modality Affinity Inference for Weakly Supervised 3D Semantic SegmentationCode0
Point Cloud Segmentation Using Transfer Learning with RandLA-Net: A Case Study on Urban Areas—0
T-MAE: Temporal Masked Autoencoders for Point Cloud Representation LearningCode0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised 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