PointLearner improves 3D point cloud perception with brain-inspired focus and context
Point-Focused Attention Meets Context-Scan State Space: Robust Biological Visual Perception for Point Cloud Representation
Computer Vision and Pattern Recognition
Summary
Representing 3D point clouds accurately is hard because you need to understand both detailed local shapes and the overall scene. The paper introduces PointLearner, which mimics how biological vision works by focusing sharply on small parts like the eye's fovea while also scanning the broader environment like eye movements. This approach helps the system better capture fine details and long-range relationships between points, making it more robust and effective. As a result, PointLearner performs very well on tasks involving 3D data.
What this means in practice
- •For 3d computer vision engineers: Design robust models that better represent both local details and global context in 3D point cloud data for improved scene understanding.
- •For autonomous vehicle developers: Enhance perception systems by using biologically inspired focus and context methods to improve point cloud data interpretation under varied conditions.
Authors
Kanglin Qu, Pan Gao, Qun Dai, Yuanhao Sun
Abstract
Synergistically capturing intricate local structures and global contextual dependencies has become a critical challenge in point cloud representation learning. To address this, we introduce PointLearner, a point cloud representation learning network that closely aligns with biological vision which employs an active, foveation-inspired processing strategy, thus enabling local geometric modeling and long-range dependency interactions simultaneously. Specifically, we first design a point-focused attention, which simulates foveal vision at the visual focus through a competitive normalized attention mechanism between local neighbors and spatially downsampled features. The spatially downsampled features are extracted by a pooling method based on learnable inducing points, which can flexibly adapt to the non-uniform distribution of point clouds as the number of inducing points is controlled and they interact directly with point clouds. Second, we propose a context-scan state space that mimics eye's saccade inference, which infers the overall semantic structure and spatial content in the scene through a scan path guided by the Hilbert curve for the bidirectional S6. With this focus-then-context biomimetic design, PointLearner demonstrates remarkable robustness and achieves state-of-the-art performance across multiple point cloud tasks.