3D Part Segmentation
61 papers with code • 2 benchmarks • 5 datasets
Segmenting 3D object parts
( Image credit: MeshCNN: A Network with an Edge )
Libraries
Use these libraries to find 3D Part Segmentation models and implementations
IntrA
LiDAR-MOS
Teeth3DS
Workshop Tools Dataset
TomoSAM
Most implemented papers
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
charlesq34/pointnet2
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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.
PointCNN: Convolution On $\mathcal{X}$-Transformed Points
yangyanli/PointCNN
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NeurIPS 2018
The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN.
PointConv: Deep Convolutional Networks on 3D Point Clouds
DylanWusee/pointconv
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CVPR 2019
Besides, our experiments converting CIFAR-10 into a point cloud showed that networks built on PointConv can match the performance of convolutional networks in 2D images of a similar structure.
PCT: Point cloud transformer
MenghaoGuo/PCT
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It is inherently permutation invariant for processing a sequence of points, making it well-suited for point cloud learning.
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.
Submanifold Sparse Convolutional Networks
facebookresearch/SparseConvNet
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Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc.
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
Yochengliu/Relation-Shape-CNN
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CVPR 2019
Specifically, the convolutional weight for local point set is forced to learn a high-level relation expression from predefined geometric priors, between a sampled point from this point set and the others.
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.
