15 citations · 22 across the 10 of their papers we have counts for
11 papers
GeoReader: Learning Latent Geometry for Spatial Reasoning in Vision-Language Models
Rong Li, Shijie Li, Jiangyong Huang +7
Vision-language models (VLMs) struggle with metric and multi-view relationships that remain implicit in RGB observations. Geometry foundation models recover this structure from RGB…
LARA: Latent Action Representation Alignment for Vision-Language-Action Models
Mengya Liu, Baoxiong Jia, Jiangyong Huang +2
Visual-language action (VLA) models enable robots to predict actions directly from observations and language instructions, but their performance depends on large-scale, high-qualit…
Lifting Unlabeled Internet-level Data for 3D Scene Understanding
Yixin Chen, Yaowei Zhang, Huangyue Yu +9
Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this paper, we demonstrate that carefully desi…
3D-RFT: Reinforcement Fine-Tuning for Video-based 3D Scene Understanding
Xiongkun Linghu, Jiangyong Huang, Baoxiong Jia +1
Reinforcement Learning with Verifiable Rewards ( RLVR ) has emerged as a transformative paradigm for enhancing the reasoning capabilities of Large Language Models ( LLMs), yet its…
SceneCOT: Eliciting Grounded Chain-of-Thought Reasoning in 3D Scenes
Xiongkun Linghu, Jiangyong Huang, Ziyu Zhu +2
Existing research on 3D Large Language Models (LLMs) still struggles to achieve grounded question-answering, primarily due to the under-exploration of the mechanism of human-like s…
LEO-VL: Efficient Scene Representation for Scalable 3D Vision-Language Learning
Jiangyong Huang, Xiaojian Ma, Xiongkun Linghu +6
Developing vision-language models (VLMs) capable of understanding 3D scenes has been a longstanding research goal. Despite recent progress, 3D VLMs still struggle with spatial reas…
Unveiling the Mist over 3D Vision-Language Understanding: Object-centric Evaluation with Chain-of-Analysis
Jiangyong Huang, Baoxiong Jia, Yan Wang +5
Existing 3D vision-language (3D-VL) benchmarks fall short in evaluating 3D-VL models, creating a "mist" that obscures rigorous insights into model capabilities and 3D-VL tasks. Thi…
Multi-modal Situated Reasoning in 3D Scenes
Xiongkun Linghu, Jiangyong Huang, Xuesong Niu +3
Situation awareness is essential for understanding and reasoning about 3D scenes in embodied AI agents. However, existing datasets and benchmarks for situated understanding are lim…
An Embodied Generalist Agent in 3D World
Jiangyong Huang, Silong Yong, Xiaojian Ma +7
Leveraging massive knowledge from large language models (LLMs), recent machine learning models show notable successes in general-purpose task solving in diverse domains such as com…
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
Ran Gong, Jiangyong Huang, Yizhou Zhao +9
Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete (e.g.,…
Perceive, Ground, Reason, and Act: A Benchmark for General-purpose Visual Representation
Jiangyong Huang, William Yicheng Zhu, Baoxiong Jia +4
Current computer vision models, unlike the human visual system, cannot yet achieve general-purpose visual understanding. Existing efforts to create a general vision model are limit…