SceneVerse: Scaling 3D Vision-Language Learning for Grounded Scene
  Understanding
Authors: name: Baoxiong Jia, name: Yixin Chen, name: Huangyue Yu, name: Yan Wang, name: Xuesong Niu, name: Tengyu Liu, name: Qing Li, name: Siyuan Huang

3D vision-language grounding, which focuses on aligning language with the 3D
physical environment, stands as a cornerstone in the development of embodied
agents. In comparison to recent advancements in the 2D domain, grounding
language in 3D scenes faces several significant challenges: (i) the inherent
complexity of 3D scenes due to the diverse object configurations, their rich
attributes, and intricate relationships; (ii) the scarcity of paired 3D
vision-language data to support grounded learning; and (iii) the absence of a
unified learning framework to distill knowledge from grounded 3D data. In this
work, we aim to address these three major challenges in 3D vision-language by
examining the potential of systematically upscaling 3D vision-language learning
in indoor environments. We introduce the first million-scale 3D vision-language
dataset, SceneVerse, encompassing about 68K 3D indoor scenes and comprising
2.5M vision-language pairs derived from both human annotations and our scalable
scene-graph-based generation approach. We demonstrate that this scaling allows
for a unified pre-training framework, Grounded Pre-training for Scenes (GPS),
for 3D vision-language learning. Through extensive experiments, we showcase the
effectiveness of GPS by achieving state-of-the-art performance on all existing
3D visual grounding benchmarks. The vast potential of SceneVerse and GPS is
unveiled through zero-shot transfer experiments in the challenging 3D
vision-language tasks. Project website: https://scene-verse.github.io .