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 226–250 of 272 papers

TitleStatusHype
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation—0
Deep FusionNet for Point Cloud Semantic Segmentation—0
Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation—0
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks—0
SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization—0
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel—0
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation—0
Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data—0
Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes—0
YOLO and K-Means Based 3D Object Detection Method on Image and Point Cloud—0
3D Object Detection Method Based on YOLO and K-Means for Image and Point Clouds—0
Indoor Point Cloud Segmentation Using Iterative Gaussian Mapping and Improved Model Fitting—0
Local Model Feature Transformations—0
Photogrammetric point cloud segmentation and object information extraction for creating virtual environments and simulations—0
Point Cloud Segmentation based on Hypergraph Spectral Clustering—0
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data—0
Deep-learning-based classification and retrieval of components of a process plant from segmented point clouds—0
LatticeNet: Fast Point Cloud Segmentation Using Permutohedral LatticesCode0
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise TransformationsCode0
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic MonitoringCode0
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D ImagesCode0
POIRot: A rotation invariant omni-directional pointnet—0
On Universal Equivariant Set NetworksCode0
On the Over-Smoothing Problem of CNN Based Disparity EstimationCode0
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers—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