PnP-3D: A Plug-and-Play for 3D Point Clouds
This work addresses the need for more effective point cloud analysis in computer vision, offering a general improvement for existing networks, though it is incremental as it builds on established methods.
The paper tackles the problem of underutilized information in point cloud data for visual analysis by proposing PnP-3D, a plug-and-play module that refines feature representations, resulting in state-of-the-art performance on four benchmarks across classification, semantic segmentation, and object detection tasks.
With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. However, there is great potential for development of these networks since the given information of point cloud data has not been fully exploited. To improve the effectiveness of existing networks in analyzing point cloud data, we propose a plug-and-play module, PnP-3D, aiming to refine the fundamental point cloud feature representations by involving more local context and global bilinear response from explicit 3D space and implicit feature space. To thoroughly evaluate our approach, we conduct experiments on three standard point cloud analysis tasks, including classification, semantic segmentation, and object detection, where we select three state-of-the-art networks from each task for evaluation. Serving as a plug-and-play module, PnP-3D can significantly boost the performances of established networks. In addition to achieving state-of-the-art results on four widely used point cloud benchmarks, we present comprehensive ablation studies and visualizations to demonstrate our approach's advantages. The code will be available at https://github.com/ShiQiu0419/pnp-3d.