Instance and Foreground Aware Single-Stage Outdoor 3D Object Detector from Point Clouds

Le résumé fourni par la source

In the context of 3D object detection solely based on point cloud data, it's crucial to extract a compact subset of representative point clouds from complex scenes. This approach offers several benefits, like reduced computational consumption and enhanced practical applicability. While the current leading algorithm for pure point cloud-based object detection has demonstrated the capability to effectively retain most foreground points, it still encounters a significant challenge: an imbalance between foreground and background points within the final point cloud. This issue results in the emergence of false positives. To address this problems, we propose a balanced detector, named IFA-SSD, which includes the foreground/background balance strategy (FBBS). In the detection algorithm based on pure point cloud type, IFA-SSD achieves the state-of-the-art in multi-class detection on KITTI and ONCE datasets.

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