VoxNet: A 3D Convolutional Neural Network for real-time object recognition. Robust object recognition is a crucial skill for robots operating autonomously in real world environments. Range sensors such as LiDAR and RGBD cameras are increasingly found in modern robotic systems, providing a rich source of 3D information that can aid in this task. However, many current systems do not fully utilize this information and have trouble efficiently dealing with large amounts of point cloud data. In this paper, we propose VoxNet, an architecture to tackle this problem by integrating a volumetric Occupancy Grid representation with a supervised 3D Convolutional Neural Network (3D CNN). We evaluate our approach on publicly available benchmarks using LiDAR, RGBD, and CAD data. VoxNet achieves accuracy beyond the state of the art while labeling hundreds of instances per second.

References in zbMATH (referenced in 12 articles )

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  1. Wei, Guangshun; Cui, Zhiming; Zhu, Jie; Yang, Lei; Zhou, Yuanfeng; Singh, Pradeep; Gu, Min; Wang, Wenping: Dense representative tooth landmark/axis detection network on 3D model (2022)
  2. 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)
  3. Ping, Yuhan; Wei, Guodong; Yang, Lei; Cui, Zhiming; Wang, Wenping: Self-attention implicit function networks for 3D dental data completion (2021)
  4. Van Biesbroeck, Antoine; Shang, Feifei; Bassir, David: CAD model segmentation via deep learning (2021)
  5. Hackel, Timo; Usvyatsov, Mikhail; Galliani, Silvano; Wegner, Jan D.; Schindler, Konrad: Inference, learning and attention mechanisms that exploit and preserve sparsity in CNNs (2020)
  6. Liu, Xinhai; Han, Zhizhong; Hong, Fangzhou; Liu, Yu-Shen; Zwicker, Matthias: LRC-net: learning discriminative features on point clouds by encoding local region contexts (2020)
  7. Pai, Gautam; Joseph-Rivlin, Mor; Kimmel, Ron; Sochen, Nir: On geometric invariants, learning, and recognition of shapes and forms (2020)
  8. Ramasinghe, Sameera; Khan, Salman; Barnes, Nick; Gould, Stephen: Representation learning on unit ball with 3D roto-translational equivariance (2020)
  9. Chen, Mingjia; Zou, Qianfang; Wang, Changbo; Liu, Ligang: EdgeNet: deep metric learning for 3D shapes (2019)
  10. 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
  11. Shaoshuai Shi, Xiaogang Wang, Hongsheng Li: PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud (2019) arXiv
  12. Ghadai, Sambit; Balu, Aditya; Sarkar, Soumik; Krishnamurthy, Adarsh: Learning localized features in 3D CAD models for manufacturability analysis of drilled holes (2018)