7 papers
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…
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…
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…
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…
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…
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…