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Multi-scale Feature Fusion with Point Pyramid for 3D Object Detection by Lu, Weihao; Zhao, Dezong; Premebida, Cristiano; Zhang, Li; Zhao, Wenjing; Tian, Daxin is a scholarly article available to read on EtoBox.

What is Multi-scale Feature Fusion with Point Pyramid for 3D Object Detection about?

Effective point cloud processing is crucial to LiDARbased autonomous driving systems. The capability to understand features at multiple scales is required for object detection of intelligent vehicles, where road users may appear in different sizes. Recent methods focus on the design of the feature aggregation operators, which collect features at different scales from the encoder backbone and assign them to the points of interest. While efforts are made into the aggregation modules, the importance of how to fuse these multi-scale features has been overlooked. This leads to insufficient feature communication across scales. To address this issue, this paper proposes the Point Pyramid RCNN (POP-RCNN), a feature pyramid-based framework for 3D object detection on point clouds. POP-RCNN consists of a Point Pyramid Feature Enhancement (PPFE) module to establish connections across spatial scales and semantic depths for information exchange. The PPFE module effectively fuses multi-scale features for rich information without the increased complexity in feature aggregation. To remedy the impact of inconsistent point densities, a point density confidence module is deployed. This design integrat

Author

Lu, Weihao; Zhao, Dezong; Premebida, Cristiano; Zhang, Li; Zhao, Wenjing; Tian, Daxin

Published

2024

Language

EN