Classification of Point Cloud Scenes with Multiscale Voxel Deep Network
arXiv:1804.03583
Abstract
In this article we describe a new convolutional neural network (CNN) to classify 3D point clouds of urban or indoor scenes. Solutions are given to the problems encountered working on scene point clouds, and a network is described that allows for point classification using only the position of points in a multi-scale neighborhood. On the reduced-8 Semantic3D benchmark [Hackel et al., 2017], this network, ranked second, beats the state of the art of point classification methods (those not using a regularization step).
arXiv commentpreprint
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Cited by in corpus (9)
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- JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds
- Differentiable Rendering: A Survey
- Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
- MeshWalker: Deep Mesh Understanding by Random Walks
- Tabulated MLP for Fast Point Feature Embedding
- 3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation
- Irregularly Tabulated MLP for Fast Point Feature Embedding
- Potential Convolution: Embedding Point Clouds into Potential Fields