In this episode, we discuss LLaVA-3D: A Simple yet Effective Pathway to Empowering LMMs with 3D-awareness by Chenming Zhu, Tai Wang, Wenwei Zhang, Jiangmiao Pang, Xihui Liu. Recent advancements in Large Multimodal Models (LMMs) have significantly improved 2D visual understanding but 3D scene understanding has lagged due to dataset and encoder limitations. The paper introduces LLaVA-3D, a framework that adapts 2D LMMs for 3D understanding by using a 3D Patch representation to link 2D features with 3D positions. This integration allows effective 3D scene understanding without losing 2D capabilities, facilitated by joint 2D and 3D vision-language instruction tuning.
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