5 papers
SceneFoundry: Generating Interactive Infinite 3D Worlds
ChunTeng Chen, YiChen Hsu, YiWen Liu +5
The ability to automatically generate large-scale, interactive, and physically realistic 3D environments is crucial for advancing robotic learning and embodied intelligence. Howeve…
Details Matter for Indoor Open-vocabulary 3D Instance Segmentation
Sanghun Jung, Jingjing Zheng, Ke Zhang +10
Unlike closed-vocabulary 3D instance segmentation that is often trained end-to-end, open-vocabulary 3D instance segmentation (OV-3DIS) often leverages vision-language models (VLMs)…
Advancing Multimodal LLMs by Large-Scale 3D Visual Instruction Dataset Generation
Liu He, Xiao Zeng, Yizhi Song +7
Multimodal Large Language Models (MLLMs) struggle with accurately capturing camera-object relations, especially for object orientation, camera viewpoint, and camera shots. This ste…
Grounding-Aware Token Pruning: Recovering from Drastic Performance Drops in Visual Grounding Caused by Pruning
Tzu-Chun Chien, Chieh-Kai Lin, Shiang-Feng Tsai +3
Recent Multimodal Large Language Models (MLLMs) have demonstrated strong performance in visual grounding, establishing themselves as a general interface for various vision-language…
Context-Aware Replanning with Pre-explored Semantic Map for Object Navigation
Po-Chen Ko, Hung-Ting Su, Ching-Yuan Chen +3
Pre-explored Semantic Maps, constructed through prior exploration using visual language models (VLMs), have proven effective as foundational elements for training-free robotic appl…