4 papers
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…