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3D Part Segmentation

9 papers with code · Computer Vision

Segmenting 3D object parts

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Greatest papers with code

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

NeurIPS 2017 charlesq34/pointnet2

By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.

3D PART SEGMENTATION

Submanifold Sparse Convolutional Networks

5 Jun 2017facebookresearch/SparseConvNet

Convolutional network are the de-facto standard for analysing spatio-temporal data such as images, videos, 3D shapes, etc.

 SOTA for 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric )

3D PART SEGMENTATION

MeshCNN: A Network with an Edge

16 Sep 2018ranahanocka/MeshCNN

In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes.

3D PART SEGMENTATION 3D SHAPE ANALYSIS CUBE ENGRAVING CLASSIFICATION

SPLATNet: Sparse Lattice Networks for Point Cloud Processing

CVPR 2018 NVlabs/splatnet

We present a network architecture for processing point clouds that directly operates on a collection of points represented as a sparse set of samples in a high-dimensional lattice.

3D PART SEGMENTATION

PointConv: Deep Convolutional Networks on 3D Point Clouds

CVPR 2019 DylanWusee/pointconv

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.

3D PART SEGMENTATION DENSITY ESTIMATION

Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models

ICCV 2017 fxia22/kdnet.pytorch

We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds.

3D PART SEGMENTATION

SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters

ECCV 2018 xyf513/SpiderCNN

Deep neural networks have enjoyed remarkable success for various vision tasks, however it remains challenging to apply CNNs to domains lacking a regular underlying structures such as 3D point clouds.

3D PART SEGMENTATION

3D Point-Capsule Networks

27 Dec 2018yongheng1991/3D-point-capsule-networks

In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.

3D FEATURE MATCHING 3D GEOMETRY PERCEPTION 3D OBJECT CLASSIFICATION 3D OBJECT RECONSTRUCTION 3D PART SEGMENTATION 3D POINT CLOUD MATCHING 3D SHAPE GENERATION 3D SHAPE REPRESENTATION