3D Semantic Segmentation
168 papers with code • 14 benchmarks • 31 datasets
3D Semantic Segmentation is a computer vision task that involves dividing a 3D point cloud or 3D mesh into semantically meaningful parts or regions. The goal of 3D semantic segmentation is to identify and label different objects and parts within a 3D scene, which can be used for applications such as robotics, autonomous driving, and augmented reality.
Benchmarks
These leaderboards are used to track progress in 3D Semantic Segmentation
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Libraries
Use these libraries to find 3D Semantic Segmentation models and implementationsDatasets
nuScenes
SemanticKITTI
S3DIS
KITTI-360
PartNet
SemanticPOSS
AMOS
RELLIS-3D
STPLS3D
nuScenes-C
Subtasks
Most implemented papers
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
charlesq34/pointnet
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CVPR 2017
Point cloud is an important type of geometric data structure.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
yanx27/Pointnet_Pointnet2_pytorch
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NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Point Transformer
Pointcept/Pointcept
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ICCV 2021
For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.
Dynamic Graph CNN for Learning on Point Clouds
WangYueFt/dgcnn
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Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.
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.
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
QingyongHu/RandLA-Net
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CVPR 2020
We study the problem of efficient semantic segmentation for large-scale 3D 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.
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
facebookresearch/SparseConvNet
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CVPR 2018
Submanifold sparse convolutional networks
