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arXiv · 2411.17761

OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection

Zhongyu Xia·Jishuo Li·Zhiwei Lin·Xinhao Wang·Yongtao Wang·Ming-Hsuan Yang
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Abstract

Open-world perception aims to develop a model adaptable to novel domains and various sensor configurations and can understand uncommon objects and corner cases. However, current research lacks sufficiently comprehensive open-world 3D perception benchmarks and robust generalizable methodologies. This paper introduces OpenAD, the first real open-world autonomous driving benchmark for 3D object detection. OpenAD is built upon a corner case discovery and annotation pipeline that integrates with a multimodal large language model (MLLM). The proposed pipeline annotates corner case objects in a unified format for five autonomous driving perception datasets with 2000 scenarios. In addition, we devise evaluation methodologies and evaluate various open-world and specialized 2D and 3D models. Moreover, we propose a vision-centric 3D open-world object detection baseline and further introduce an ensemble method by fusing general and specialized models to address the issue of lower precision in existing open-world methods for the OpenAD benchmark. We host an online challenge on EvalAI. Data, toolkit codes, and evaluation codes are available at https://github.com/VDIGPKU/OpenAD.

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Zhongyu Xia, Jishuo Li, Zhiwei Lin, Xinhao Wang, Yongtao Wang, Ming-Hsuan Yang. 2025-05-26. OpenAD: Open-World Autonomous Driving Benchmark for 3D Object Detection. https://arxiv.org/abs/2411.17761

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