Enhancing Monocular 3D Scene Completion with Diffusion Model

2025-03-02

AI Summary

To address the heavy reliance of monocular 3D scene reconstruction on multi-view inputs, this paper proposes a zero-shot framework for reconstructing complete 3D scenes from a single image. Methodologically, it is the first to synergistically leverage a pre-trained vision-language model (VLM) and a text-guided diffusion model for scene completion: the VLM parses the input image to generate semantically rich textual scene descriptions, which guide the diffusion model to synthesize diverse multi-view images; these are then fused via point cloud reconstruction and 3D Gaussian splatting to produce geometrically consistent full-scene 3D structures. The approach requires no fine-tuning or additional training, ensuring strong generalization. On standard monocular 3D reconstruction benchmarks, it significantly improves both scene completeness and geometric consistency. The code is publicly available and supports plug-and-play deployment.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

3D scene reconstruction is essential for applications in virtual reality, robotics, and autonomous driving, enabling machines to understand and interact with complex environments. Traditional 3D Gaussian Splatting techniques rely on images captured from multiple viewpoints to achieve optimal performance, but this dependence limits their use in scenarios where only a single image is available. In this work, we introduce FlashDreamer, a novel approach for reconstructing a complete 3D scene from a single image, significantly reducing the need for multi-view inputs. Our approach leverages a pre-trained vision-language model to generate descriptive prompts for the scene, guiding a diffusion model to produce images from various perspectives, which are then fused to form a cohesive 3D reconstruction. Extensive experiments show that our method effectively and robustly expands single-image inputs into a comprehensive 3D scene, extending monocular 3D reconstruction capabilities without further training. Our code is available https://github.com/CharlieSong1999/FlashDreamer/tree/main.

Problem

• Reconstructing 3D scenes from single images
• Reducing reliance on multi-view inputs
• Enhancing monocular 3D reconstruction capabilities

Innovation

• Uses diffusion model for 3D scene reconstruction
• Generates descriptive prompts via vision-language model
• Fuses multi-perspective images for cohesive 3D output