LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sight
Man, Yunze, Wang, Shihao, Zhang, Guowen, Bjorck, Johan, Gui, Liang-Yan, Fan, Jim, Kautz, Jan, Wang, Yu-Xiong 等 9 位作者
原文摘要
To act in the world, a model must name what it sees and know where it is in 3D. Today's vision-language models excel at open-ended 2D description and grounding, yet multi-object 3D detection remains largely missing from the VLM toolbox. We present LocateAnything3D, a VLM-native recipe that casts 3D detection as a next-token prediction problem. The key is a short, explicit Chain-of-Sight (CoS) sequence that mirrors how people reason from images: find an object in 2D, then infer its distance, size, and pose. The decoder first emits 2D detections as a visual chain-of-thought, then predicts 3D boxes under an easy-to-hard curriculum: across objects, a near-to-far order reduces early ambiguity and matches ego-centric utility; within each object, a center-from-camera, dimensions, and rotation factorization ranks information by stability and learnability. This VLM-native interface preserves open-vocabulary and visual-prompting capability without specialized heads. On the challenging Omni3D benchmark, our model achieves state-of-the-art results, with 49.89 AP, surpassing the previous best by +15.51 absolute improvement even when baseline is given ground-truth 2D boxes. It also generalizes zero-shot to held-out categories with strong calibration and robustness. By turning 3D detection into a disciplined next-token problem, LocateAnything3D offers a practical foundation for models to perceive in 3D.
cs.CV · cs.CL
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表