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3D Semantic Segmentation

13 papers with code · Computer Vision

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DeepGCNs: Can GCNs Go as Deep as CNNs?

ICCV 2019 2019 lightaime/deep_gcns

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.

3D PART SEGMENTATION 3D SEMANTIC SEGMENTATION NODE CLASSIFICATION

DeepGCNs: Can GCNs Go as Deep as CNNs?

ICCV 2019 lightaime/deep_gcns

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.

3D SEMANTIC SEGMENTATION

4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

CVPR 2019 StanfordVL/MinkowskiEngine

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.

3D SEMANTIC SEGMENTATION 4D SPATIO TEMPORAL SEMANTIC SEGMENTATION

Associatively Segmenting Instances and Semantics in Point Clouds

CVPR 2019 WXinlong/ASIS

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.

3D INSTANCE SEGMENTATION 3D SEMANTIC SEGMENTATION

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

ICCV 2019 PRBonn/semantic-kitti-api

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.

3D SEMANTIC SEGMENTATION SCENE UNDERSTANDING SELF-DRIVING CARS

RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds

25 Nov 2019QingyongHu/RandLA-Net

We study the problem of efficient semantic segmentation for large-scale 3D point clouds.

3D SEMANTIC SEGMENTATION

TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes

CVPR 2019 hjwdzh/TextureNet

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).

3D SEMANTIC SEGMENTATION