Péter Hajas | Searcharxiv

arXiv2026

Zero-shot 3D General Obstacle Detection via Multimodal Foundation Models and Geometry

Detecting general obstacles is critical for autonomous driving, especially in long-tail scenarios with rare or unseen objects. Existing methods rely on supervision or predefined categories, limiting generalization. We propose a training-free approach that combines multimodal foundation models with geometric reasoning for 3D obstacle detection. Our key idea is to detect obstacles as deviations from the road surface, segmented in 2D and localized in 3D via temporal LiDAR aggregation. The pipeline operates in a zero-shot manner without task-specific training. Experiments show accurate localization up to 100 meters and 10-25% recall gains from foundation model priors, while enabling scalable autolabeling.

cs.CV↗

arXiv2023

aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception

Autonomous driving is a popular research area within the computer vision research community. Since autonomous vehicles are highly safety-critical, ensuring robustness is essential for real-world deployment. While several public multimodal datasets are accessible, they mainly comprise two sensor modalities (camera, LiDAR) which are not well suited for adverse weather. In addition, they lack far-range annotations, making it harder to train neural networks that are the base of a highway assistant function of an autonomous vehicle. Therefore, we introduce a multimodal dataset for robust autonomous driving with long-range perception. The dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view. The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain and is annotated with 3D bounding boxes with consistent identifiers across frames. Furthermore, we trained unimodal and multimodal baseline models for 3D object detection. Data are available at \url{https://github.com/aimotive/aimotive_dataset}.

cs.CV↗