Attention Models for Point Clouds in Deep Learning: A Survey
arXiv:2102.10788
Abstract
Recently, the advancement of 3D point clouds in deep learning has attracted intensive research in different application domains such as computer vision and robotic tasks. However, creating feature representation of robust, discriminative from unordered and irregular point clouds is challenging. In this paper, our ultimate goal is to provide a comprehensive overview of the point clouds feature representation which uses attention models. More than 75+ key contributions in the recent three years are summarized in this survey, including the 3D objective detection, 3D semantic segmentation, 3D pose estimation, point clouds completion etc. We provide a detailed characterization (1) the role of attention mechanisms, (2) the usability of attention models into different tasks, (3) the development trend of key technology.
References in corpus (6)
- Deep Closest Point: Learning Representations for Point Cloud Registration
- PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
- PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition
- PWCLO-Net: Deep LiDAR Odometry in 3D Point Clouds Using Hierarchical Embedding Mask Optimization
- MANet: Multimodal Attention Network based Point- View fusion for 3D Shape Recognition
- PAM:Point-wise Attention Module for 6D Object Pose Estimation