3D Semantic Segmentation
153 papers with code • 12 benchmarks • 29 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find 3D Semantic Segmentation models and implementationsDatasets
nuScenes
SemanticKITTI
S3DIS
PartNet
KITTI-360
SemanticPOSS
AMOS
STPLS3D
RELLIS-3D
nuScenes-C
Subtasks
Most implemented papers
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
charlesq34/pointnet
•
•
CVPR 2017
Point cloud is an important type of geometric data structure.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
charlesq34/pointnet2
•
•
NeurIPS 2017
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Point Transformer
Pointcept/Pointcept
•
•
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
•
•
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
•
•
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
•
•
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
•
•
CVPR 2020
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
facebookresearch/SparseConvNet
•
•
CVPR 2018
Submanifold sparse convolutional networks
PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understanding
yangyanli/PointCNN
•
•
CVPR 2019
We present PartNet: a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information.
