Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness

• URL: http://arxiv.org/abs/2504.01901v1
• Date: Wed, 02 Apr 2025 16:59:55 GMT
• Title: Ross3D: Reconstructive Visual Instruction Tuning with 3D-Awareness
• Authors: Haochen Wang, Yucheng Zhao, Tiancai Wang, Haoqiang Fan, Xiangyu Zhang, Zhaoxiang Zhang,
• Abstract summary: We introduce reconstructive visual instruction tuning with 3D-awareness (Ross3D), which integrates 3D-aware visual supervision into the training procedure.<n>Ross3D achieves state-of-the-art performance across various 3D scene understanding benchmarks.
• Score: 73.72335146374543
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid development of Large Multimodal Models (LMMs) for 2D images and videos has spurred efforts to adapt these models for interpreting 3D scenes. However, the absence of large-scale 3D vision-language datasets has posed a significant obstacle. To address this issue, typical approaches focus on injecting 3D awareness into 2D LMMs by designing 3D input-level scene representations. This work provides a new perspective. We introduce reconstructive visual instruction tuning with 3D-awareness (Ross3D), which integrates 3D-aware visual supervision into the training procedure. Specifically, it incorporates cross-view and global-view reconstruction. The former requires reconstructing masked views by aggregating overlapping information from other views. The latter aims to aggregate information from all available views to recover Bird's-Eye-View images, contributing to a comprehensive overview of the entire scene. Empirically, Ross3D achieves state-of-the-art performance across various 3D scene understanding benchmarks. More importantly, our semi-supervised experiments demonstrate significant potential in leveraging large amounts of unlabeled 3D vision-only data.

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