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 151–200 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
Language-Level Semantics Conditioned 3D Point Cloud Segmentation—0
SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud SegmentationCode1
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance—0
DLA-Net: Learning Dual Local Attention Features for Semantic Segmentation of Large-Scale Building Facade Point CloudsCode1
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
Omni-supervised Point Cloud Segmentation via Gradual Receptive Field Component ReasoningCode1
Weakly Supervised Pseudo-Label assisted Learning for ALS Point Cloud Semantic Segmentation—0
Cross-Level Cross-Scale Cross-Attention Network for Point Cloud Representation—0
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation NetworkCode1
Segmentation of EM showers for neutrino experiments with deep graph neural networksCode0
PAConv: Position Adaptive Convolution with Dynamic Kernel Assembling on Point CloudsCode1
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation—0
LRGNet: Learnable Region Growing for Class-Agnostic Point Cloud SegmentationCode1
Deep Learning Based 3D Segmentation: A Survey—0
FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud SegmentationCode1
From a Point Cloud to a Simulation Model: Bayesian Segmentation and Entropy based Uncertainty Estimation for 3D Modelling—0
PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation—0
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach—0
Deep Parametric Continuous Convolutional Neural Networks—0
Boundary-Aware Geometric Encoding for Semantic Segmentation of Point CloudsCode0
TempNet: Online Semantic Segmentation on Large-Scale Point Cloud Series—0
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation—0
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point CloudCode1
Point TransformerCode1
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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