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 151160 of 272 papers

TitleStatusHype
Projection-based Point Convolution for Efficient Point Cloud SegmentationCode0
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation0
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph PropagationCode0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation0
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud SegmentationCode1
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Point Cloud Segmentation Using Sparse Temporal Local Attention0
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point ModelingCode1
AGCN: Adversarial Graph Convolutional Network for 3D 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