RoPETR: Improving Temporal Camera-Only 3D Detection by Integrating Enhanced Rotary Position Embedding

• URL: http://arxiv.org/abs/2504.12643v2
• Date: Fri, 18 Apr 2025 06:53:59 GMT
• Title: RoPETR: Improving Temporal Camera-Only 3D Detection by Integrating Enhanced Rotary Position Embedding
• Authors: Hang Ji, Tao Ni, Xufeng Huang, Tao Luo, Xin Zhan, Junbo Chen,
• Abstract summary: This report introduces a targeted improvement to the StreamPETR framework, specifically aimed at enhancing velocity estimation.<n>Our improved approach achieves a state-of-the-art NDS of 70.86% using the ViT-L backbone, setting a new benchmark for camera-only 3D object detection.
• Score: 7.142677515668237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This technical report introduces a targeted improvement to the StreamPETR framework, specifically aimed at enhancing velocity estimation, a critical factor influencing the overall NuScenes Detection Score. While StreamPETR exhibits strong 3D bounding box detection performance as reflected by its high mean Average Precision our analysis identified velocity estimation as a substantial bottleneck when evaluated on the NuScenes dataset. To overcome this limitation, we propose a customized positional embedding strategy tailored to enhance temporal modeling capabilities. Experimental evaluations conducted on the NuScenes test set demonstrate that our improved approach achieves a state-of-the-art NDS of 70.86% using the ViT-L backbone, setting a new benchmark for camera-only 3D object detection.

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