Robust 3D Semantic Segmentation
17 papers with code • 3 benchmarks • 3 datasets
3D Semantic Segmentation under Out-of-Distribution Scenarios
Benchmarks
These leaderboards are used to track progress in Robust 3D Semantic Segmentation
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Libraries
Use these libraries to find Robust 3D Semantic Segmentation models and implementations
nuScenes-C
SemanticKITTI-C
WOD-C
Most implemented papers
KPConv: Flexible and Deformable Convolution for Point Clouds
HuguesTHOMAS/KPConv
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ICCV 2019
Furthermore, these locations are continuous in space and can be learned by the network.
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
StanfordVL/MinkowskiEngine
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CVPR 2019
To overcome challenges in the 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and the trilateral-stationary conditional random field that enforces spatio-temporal consistency in the 7D space-time-chroma space.
SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud
In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds.
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
mit-han-lab/spvnas
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ECCV 2020
Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely.
SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time.
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
edwardzhou130/PolarSeg
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CVPR 2020
The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR.
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
LiDAR semantic segmentation essential for advanced autonomous driving is required to be accurate, fast, and easy-deployed on mobile platforms.
RangeNet++: Fast and Accurate LiDAR Semantic Segmentation
PRBonn/lidar-bonnetal
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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2019
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation
xinge008/Cylinder3D
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CVPR 2021
However, we found that in the outdoor point cloud, the improvement obtained in this way is quite limited.
CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving
Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation.
