DCDet: Dynamic Cross-based 3D Object Detector

• URL: http://arxiv.org/abs/2401.07240v2
• Date: Wed, 22 May 2024 06:51:34 GMT
• Title: DCDet: Dynamic Cross-based 3D Object Detector
• Authors: Shuai Liu, Boyang Li, Zhiyu Fang, Kai Huang,
• Abstract summary: Alternative label assignment strategies remain unexplored in 3D object detection. We introduce a dynamic cross label assignment (DCLA) scheme, which dynamically assigns positive samples for each object from a cross-shaped region. We also put forth a rotation-weighted Intersection over Union (RWIoU) metric to replace the widely used L1 metric in regression loss.
• Score: 9.13446805857794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, significant progress has been made in the research of 3D object detection. However, most prior studies have focused on the utilization of center-based or anchor-based label assignment schemes. Alternative label assignment strategies remain unexplored in 3D object detection. We find that the center-based label assignment often fails to generate sufficient positive samples for training, while the anchor-based label assignment tends to encounter an imbalanced issue when handling objects of varying scales. To solve these issues, we introduce a dynamic cross label assignment (DCLA) scheme, which dynamically assigns positive samples for each object from a cross-shaped region, thus providing sufficient and balanced positive samples for training. Furthermore, to address the challenge of accurately regressing objects with varying scales, we put forth a rotation-weighted Intersection over Union (RWIoU) metric to replace the widely used L1 metric in regression loss. Extensive experiments demonstrate the generality and effectiveness of our DCLA and RWIoU-based regression loss. The Code will be available at https://github.com/Say2L/DCDet.git.

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