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Submanifold sparse convolutional networks
#2 best model for Semantic Segmentation on ScanNet
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.
To overcome challenges in the 4D space, we propose the hybrid kernel, a special case of the generalized sparse convolution, and the trilateral-stationary conditional random field that enforces spatio-temporal consistency in the 7D space-time-chroma space.
SOTA for Semantic Segmentation on ScanNet
A 3D point cloud describes the real scene precisely and intuitively. To date how to segment diversified elements in such an informative 3D scene is rarely discussed.
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images.
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
SOTA for Semantic Segmentation on Semantic3D
We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e. g., color texture maps).
#3 best model for Semantic Segmentation on ScanNet