LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sight

• URL: http://arxiv.org/abs/2511.20648v1
• Date: Tue, 25 Nov 2025 18:59:45 GMT
• Title: LocateAnything3D: Vision-Language 3D Detection with Chain-of-Sight
• Authors: Yunze Man, Shihao Wang, Guowen Zhang, Johan Bjorck, Zhiqi Li, Liang-Yan Gui, Jim Fan, Jan Kautz, Yu-Xiong Wang, Zhiding Yu,
• Abstract summary: We present a VLM-native recipe that casts 3D detection as a next-token prediction problem.<n>Our model achieves state-of-the-art results, with 49.89 AP_3D, surpassing the previous best by +15.51 absolute improvement.
• Score: 105.9472902251177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To act in the world, a model must name what it sees and know where it is in 3D. Today's vision-language models (VLMs) 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 human 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_3D, surpassing the previous best by +15.51 absolute improvement even when the baseline is given ground-truth 2D boxes. It also generalizes zero-shot to held-out categories with strong robustness. By turning 3D detection into a disciplined next-token problem, LocateAnything3D offers a practical foundation for models to perceive in 3D.

Related papers

• Does Your 3D Encoder Really Work? When Pretrain-SFT from 2D VLMs Meets 3D VLMs [72.11701578308804]
  This paper categorizes recent 3D Vision-Language Models into 3D object-centric, 2D image-based, and 3D scene-centric approaches.<n>Despite the architectural similarity of 3D scene-centric VLMs to their 2D counterparts, they have exhibited comparatively lower performance compared with the latest 3D object-centric and 2D image-based approaches.<n>Our investigation suggests that while these models possess cross-modal alignment capabilities, they tend to over-rely on linguistic cues and overfit to frequent answer distributions.
  arXiv  Detail & Related papers  (2025-06-05T17:56:12Z)
• 3D Question Answering via only 2D Vision-Language Models [87.41421075243103]
  Large vision-language models (LVLMs) have advanced numerous fields.<n>We explore how to harness their potential to address 3D scene understanding tasks, using 3D question answering (3D-QA) as a representative example.<n>Specifically, we sample 2D views from a 3D point cloud and feed them into 2D models to answer a given question.<n>We propose cdViews, a novel approach to automatically selecting critical and diverse Views for 3D-QA.
  arXiv  Detail & Related papers  (2025-05-28T09:04:39Z)
• Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D [68.23391872643268]
  LOCATE 3D is a model for localizing objects in 3D scenes from referring expressions like "the small coffee table between the sofa and the lamp"<n>It operates directly on sensor observation streams (posed RGB-D frames), enabling real-world deployment on robots and AR devices.
  arXiv  Detail & Related papers  (2025-04-19T02:51:24Z)
• Detect Anything 3D in the Wild [34.293450721860616]
  We introduce DetAny3D, a promptable 3D detection foundation model capable of detecting any novel object under arbitrary camera configurations.<n>To effectively transfer 2D knowledge to 3D, DetAny3D incorporates two core modules: the 2D Aggregator and the 3D Interpreter with Zero-Embedding Mapping.<n> Experimental results validate the strong generalization of our DetAny3D, which achieves state-of-the-art performance on unseen categories and novel camera configurations.
  arXiv  Detail & Related papers  (2025-04-10T17:59:22Z)
• 3D-LLM: Injecting the 3D World into Large Language Models [60.43823088804661]
  Large language models (LLMs) and Vision-Language Models (VLMs) have been proven to excel at multiple tasks, such as commonsense reasoning. We propose to inject the 3D world into large language models and introduce a new family of 3D-LLMs. Specifically, 3D-LLMs can take 3D point clouds and their features as input and perform a diverse set of 3D-related tasks.
  arXiv  Detail & Related papers  (2023-07-24T17:59:02Z)
• FCOS3D: Fully Convolutional One-Stage Monocular 3D Object Detection [78.00922683083776]
  It is non-trivial to make a general adapted 2D detector work in this 3D task. In this technical report, we study this problem with a practice built on fully convolutional single-stage detector. Our solution achieves 1st place out of all the vision-only methods in the nuScenes 3D detection challenge of NeurIPS 2020.
  arXiv  Detail & Related papers  (2021-04-22T09:35:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.