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.

翻译：三维视觉-语言定位聚焦于将语言与三维物理环境对齐，是具身智能体发展的基石。相较于二维领域的最新进展，三维场景中的语言定位面临三大关键挑战：（i）三维场景因物体配置多样性、丰富属性及复杂关系而具有内在复杂性；（ii）支撑定位学习的配准三维视觉-语言数据稀缺；（iii）缺乏从三维数据中提炼知识的统一学习框架。本研究旨在通过系统探索室内环境中三维视觉-语言学习的规模化潜力，应对上述三大挑战。我们首次提出百万级三维视觉-语言数据集SceneVerse，包含约6.8万室内三维场景及250万视觉-语言对（整合人工标注与基于场景图的可扩展生成方法）。实验证明，该规模化方法支撑起统一预训练框架GPS（Grounded Pre-training for Scenes），用于三维视觉-语言学习。通过广泛实验，GPS在所有现有三维视觉定位基准上均实现最优性能。在具有挑战性的三维视觉-语言零样本迁移实验中，SceneVerse与GPS展现出巨大潜力。项目网站：https://scene-verse.github.io。