Segmenting 3D object parts
( Image credit: MeshCNN: A Network with an Edge )
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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.
SOTA for Semantic Segmentation on S3DIS (Accuracy 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.
Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3. 7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation.
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.
#3 best model for 3D Part Segmentation on ShapeNet-Part
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.
#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.
Point clouds are unstructured and unordered data, as opposed to images.
#4 best model for 3D Part Segmentation on ShapeNet-Part