3D Part Segmentation

61 papers with code • 2 benchmarks • 5 datasets

Segmenting 3D object parts

( Image credit: MeshCNN: A Network with an Edge )


Use these libraries to find 3D Part Segmentation models and implementations

Most implemented papers

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 24 Jan 2018

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 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 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 17 Dec 2020

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 ICCV 2019

Furthermore, these locations are continuous in space and can be learned by the network.

Submanifold Sparse Convolutional Networks

facebookresearch/SparseConvNet 5 Jun 2017

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 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

sitzikbs/3DmFV-Net 22 Nov 2017

The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.