OctNet: Learning Deep 3D Representations at High Resolutions. We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.
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References in zbMATH (referenced in 11 articles )
Showing results 1 to 11 of 11.
- Ghadai, Sambit; Lee, Xian Yeow; Balu, Aditya; Sarkar, Soumik; Krishnamurthy, Adarsh: Multi-resolution 3D CNN for learning multi-scale spatial features in CAD models (2021)
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- Chen, Mingjia; Zou, Qianfang; Wang, Changbo; Liu, Ligang: EdgeNet: deep metric learning for 3D shapes (2019)
- Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, Juergen Gall: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences (2019) arXiv
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li: PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud (2019) arXiv
- Ghadai, Sambit; Balu, Aditya; Sarkar, Soumik; Krishnamurthy, Adarsh: Learning localized features in 3D CAD models for manufacturability analysis of drilled holes (2018)