MoniRefer: A New Dataset for 3D Visual Grounding in Roadside Infrastructure
Research Paper#Computer Vision, 3D Visual Grounding, Roadside Infrastructure, Multi-modal Learning🔬 Research|Analyzed: Jan 3, 2026 08:53•
Published: Dec 31, 2025 03:56
•1 min read
•ArXivAnalysis
This paper introduces a novel dataset, MoniRefer, for 3D visual grounding specifically tailored for roadside infrastructure. This is significant because existing datasets primarily focus on indoor or ego-vehicle perspectives, leaving a gap in understanding traffic scenes from a broader, infrastructure-level viewpoint. The dataset's large scale and real-world nature, coupled with manual verification, are key strengths. The proposed method, Moni3DVG, further contributes to the field by leveraging multi-modal data for improved object localization.
Key Takeaways
- •Introduces MoniRefer, a new large-scale dataset for 3D visual grounding in roadside infrastructure.
- •Addresses the gap in existing datasets by focusing on infrastructure-level understanding of traffic scenes.
- •Proposes Moni3DVG, a new end-to-end method for multi-modal feature learning and 3D object localization.
- •The dataset and code will be released, promoting further research in this area.
Reference / Citation
View Original"“...the first real-world large-scale multi-modal dataset for roadside-level 3D visual grounding.”"