Deep Learning for 3D Point Cloud Understanding: A Survey
It provides a comprehensive overview for researchers and practitioners in fields like autonomous driving and robotics, but it is incremental as it synthesizes existing work without introducing new methods.
This survey paper tackles the problem of summarizing recent progress in deep learning for 3D point cloud understanding, covering various tasks like classification and segmentation, and compiling datasets and state-of-the-art performances.
The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unstructured and noisy 3D points. To demonstrate the latest progress of deep learning for 3D point cloud understanding, this paper summarizes recent remarkable research contributions in this area from several different directions (classification, segmentation, detection, tracking, flow estimation, registration, augmentation and completion), together with commonly used datasets, metrics and state-of-the-art performances. More information regarding this survey can be found at: https://github.com/SHI-Labs/3D-Point-Cloud-Learning.