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 51–75 of 272 papers

TitleStatusHype
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud SegmentationCode1
Lidar Panoptic Segmentation in an Open WorldCode1
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
PointNet: Deep Learning on Point Sets for 3D Classification and SegmentationCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
Human Vision Based 3D Point Cloud Semantic Segmentation of Large-Scale Outdoor SceneCode1
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point CloudsCode1
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
PointHR: Exploring High-Resolution Architectures for 3D Point Cloud SegmentationCode1
Point TransformerCode1
Dynamic Graph CNN for Learning on Point CloudsCode1
Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud SegmentationCode1
BPNet: Bézier Primitive Segmentation on 3D Point CloudsCode1
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point ModelingCode1
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud SegmentationCode1
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
Boosting Few-shot 3D Point Cloud Segmentation via Query-Guided EnhancementCode1
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point CloudsCode1
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic SegmentationCode1
Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?Code1
Generalized Few-Shot Point Cloud Segmentation Via Geometric WordsCode1
PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic SegmentationCode1
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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