Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision
  Geometry Priors
Previous research has investigated the application of Multimodal Large
Language Models (MLLMs) in understanding 3D scenes by interpreting them as
videos. These approaches generally depend on comprehensive 3D data inputs, such
as point clouds or reconstructed Bird's-Eye View (BEV) maps. In our research,
we advance this field by enhancing the capability of MLLMs to understand and
reason in 3D spaces directly from video data, without the need for additional
3D input. We propose a novel and efficient method, the Video-3D Geometry Large
Language Model (VG LLM). Our approach employs a 3D visual geometry encoder that
extracts 3D prior information from video sequences. This information is
integrated with visual tokens and fed into the MLLM. Extensive experiments have
shown that our method has achieved substantial improvements in various tasks
related to 3D scene understanding and spatial reasoning, all directly learned
from video sources. Impressively, our 4B model, which does not rely on explicit
3D data inputs, achieves competitive results compared to existing
state-of-the-art methods, and even surpasses the Gemini-1.5-Pro in the
VSI-Bench evaluations.
683e5569fce31842c60675d7
Duo Zheng
claimed_verified
2025-06-03T08:46:45.196Z
646e2fcaf813cfe153f1af6c
/avatars/2f87d0e5c071000990da29cd744bc03d.svg
Duo Zheng
user
zd11024
683e5569fce31842c60675d8
Shijia Huang
683e5569fce31842c60675d9
Yanyang Li
683e5569fce31842c60675da
Liwei Wang
Learning from Videos for 3D World: Enhancing MLLMs with 3D Vision
  Geometry Priors
Previous research has investigated the application of Multimodal Large
Language Models (MLLMs) in understanding 3D scenes by interpreting them as
videos. These approaches generally depend on comprehensive 3D data inputs, such
as point clouds or reconstructed Bird's-Eye View (BEV) maps. In our research,
we advance this field by enhancing the capability of MLLMs to understand and
reason in 3D spaces directly from video data, without the need for additional
3D input. We propose a novel and efficient method, the Video-3D Geometry Large
Language Model (VG LLM). Our approach employs a 3D visual geometry encoder that
extracts 3D prior information from video sequences. This information is
integrated with visual tokens and fed into the MLLM. Extensive experiments have
shown that our method has achieved substantial improvements in various tasks
related to 3D scene understanding and spatial reasoning, all directly learned
from video sources. Impressively, our 4B model, which does not rely on explicit
3D data inputs, achieves competitive results compared to existing
state-of-the-art methods, and even surpasses the Gemini-1.5-Pro in the
VSI-Bench evaluations.
A novel Video-3D Geometry Large Language Model (VG LLM) extracts 3D information directly from video sequences to enhance 3D scene understanding without additional 3D data, achieving competitive results in various tasks.
MLLMs Video-3D Geometry Large Language Model VG LLM 3D visual geometry encoder VSI-Bench