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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–175 of 272 papers

TitleStatusHype
Projection-based Point Convolution for Efficient Point Cloud SegmentationCode0
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation—0
Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph PropagationCode0
An MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation—0
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud SegmentationCode1
PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving—0
On Adversarial Robustness of Point Cloud Semantic SegmentationCode0
Point Cloud Segmentation Using Sparse Temporal Local Attention—0
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point ModelingCode1
AGCN: Adversarial Graph Convolutional Network for 3D Point Cloud SegmentationCode1
DRINet++: Efficient Voxel-as-point Point Cloud Segmentation—0
Background-Aware 3D Point Cloud Segmentationwith Dynamic Point Feature Aggregation—0
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation—0
Continuous Conditional Random Field Convolution for Point Cloud SegmentationCode1
Occlusion-robust Visual Markerless Bone Tracking for Computer-Assisted Orthopaedic Surgery—0
3D point cloud segmentation using GIS—0
LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices—0
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation—0
Learning with Noisy Labels for Robust Point Cloud SegmentationCode1
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds—0
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds—0
VIN: Voxel-based Implicit Network for Joint 3D Object Detection and Segmentation for Lidars—0
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor—0
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning—0
Scalable Certified Segmentation via Randomized SmoothingCode0
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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