Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding

arXiv:2404.07989v1 Announce Type: cross
Abstract: Large foundation models have recently emerged as a prominent focus of interest, attaining superior performance in widespread scenarios. Due to the scarcity of 3D data, many efforts have been made to adapt pre-trained transformers from vision to 3D domains. However, such 2D-to-3D approaches are still limited, due to the potential loss of spatial geometries and high computation cost. More importantly, their frameworks are mainly designed for 2D models, lacking a general any-to-3D paradigm. In this …

arxiv cs.ai cs.cl cs.cv cs.lg cs.sd eess.as large models type understanding