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 161170 of 272 papers

TitleStatusHype
3DSES: an indoor Lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model0
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation0
Adversarially Masking Synthetic To Mimic Real: Adaptive Noise Injection for Point Cloud Segmentation Adaptation0
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving0
An Experimental Study of SOTA LiDAR Segmentation Models0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation0
An optimal hierarchical clustering approach to segmentation of mobile LiDAR point clouds0
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation0
Automated Image-Based Identification and Consistent Classification of Fire Patterns with Quantitative Shape Analysis and Spatial Location Identification0
Autonomous Point Cloud Segmentation for Power Lines Inspection in Smart Grid0
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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