VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D
  Reconstruction
Authors: name: Zhiwen Fan; name: Jian Zhang; name: Renjie Li; name: Junge Zhang; name: Runjin Chen; name: Hezhen Hu; name: Kevin Wang; name: Huaizhi Qu; name: Dilin Wang; name: Zhicheng Yan; name: Hongyu Xu; name: Justin Theiss; name: Tianlong Chen; name: Jiachen Li; name: Zhengzhong Tu; name: Zhangyang Wang; name: Rakesh Ranjan
Published: 2025-05-26 17:56:30

Abstract: The rapid advancement of Large Multimodal Models (LMMs) for 2D images and
videos has motivated extending these models to understand 3D scenes, aiming for
human-like visual-spatial intelligence. Nevertheless, achieving deep spatial
understanding comparable to human capabilities poses significant challenges in
model encoding and data acquisition. Existing methods frequently depend on
external depth sensors for geometry capture or utilize off-the-shelf algorithms
for pre-constructing 3D maps, thereby limiting their scalability, especially
with prevalent monocular video inputs and for time-sensitive applications. In
this work, we introduce VLM-3R, a unified framework for Vision-Language Models
(VLMs) that incorporates 3D Reconstructive instruction tuning. VLM-3R processes
monocular video frames by employing a geometry encoder to derive implicit 3D
tokens that represent spatial understanding. Leveraging our Spatial-Visual-View
Fusion and over 200K curated 3D reconstructive instruction tuning
question-answer (QA) pairs, VLM-3R effectively aligns real-world spatial
context with language instructions. This enables monocular 3D spatial
assistance and embodied reasoning. To facilitate the evaluation of temporal
reasoning, we introduce the Vision-Spatial-Temporal Intelligence benchmark,
featuring over 138.6K QA pairs across five distinct tasks focused on evolving
spatial relationships. Extensive experiments demonstrate that our model,
VLM-3R, not only facilitates robust visual-spatial reasoning but also enables
the understanding of temporal 3D context changes, excelling in both accuracy
and scalability.