Segmenting 3D object parts
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Point cloud is an important type of geometric data structure.
#2 best model for Scene Segmentation on ScanNet
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
#2 best model for Semantic Segmentation on ShapeNet
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 )
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.
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.
#3 best model for 3D Part Segmentation on ShapeNet-Part
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.
We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds.
#6 best model for 3D Part Segmentation on ShapeNet-Part
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.
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.
#2 best model for 3D Part Segmentation on ShapeNet-Part