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Point cloud is an important type of geometric data structure.
Ranked #2 on Scene Segmentation on ScanNet
By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.
Ranked #2 on Semantic Segmentation on ShapeNet
Submanifold sparse convolutional networks
Ranked #3 on Semantic Segmentation on ScanNet
Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene.
Ranked #1 on 3D Reconstruction on ScanNet
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.
Ranked #2 on 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.
Ranked #12 on Semantic Segmentation on S3DIS
We study the problem of efficient semantic segmentation for large-scale 3D point clouds.
Ranked #1 on Semantic Segmentation on Semantic3D
We propose a novel deep learning-based framework to tackle the challenge of semantic segmentation of large-scale point clouds of millions of points.
Ranked #1 on Semantic Segmentation on Semantic3D (oAcc metric)
In this paper, we address semantic segmentation of road-objects from 3D LiDAR point clouds.
Ranked #18 on 3D Semantic Segmentation on SemanticKITTI
Perception in autonomous vehicles is often carried out through a suite of different sensing modalities.
Ranked #14 on 3D Semantic Segmentation on SemanticKITTI