Coherent 3D Scene Diffusion From a Single RGB Image
Diffusion-based 3D scene reconstruction from a single RGB image, achieving 12.04% AP3D and 13.43% F-Score improvements.
Key Findings
Methodology
This paper introduces a conditional diffusion framework that jointly predicts poses and geometries of all scene objects from a single RGB image. The model learns a scene prior by conditioning on all objects simultaneously, capturing inter-object relationships via multi-head attention modules. It employs a U-Net-based denoising network for pose estimation and a Transformer for shape encoding, integrated with an intra-scene attention mechanism. During training, a surface alignment loss based on sampled points from intermediate shape representations guides joint pose-shape optimization, even with partial supervision. The approach leverages a denoising diffusion process with classifier-free guidance, enabling robust scene generation and refinement, validated on SUN RGB-D and Pix3D datasets.
Key Results
- On SUN RGB-D, our method improves AP3D by 12.04%, reaching 36.10%, and reduces surface alignment loss by 0.36. On Pix3D, F-Score increases by 13.43%, with a Chamfer distance of 15.05. Ablation studies confirm the effectiveness of scene prior modeling, relationship attention, and diffusion training over regression baselines.
- The model demonstrates superior scene coherence, avoiding object intersections and floating artifacts, with sharper geometries and more accurate poses. Cross-dataset tests show strong generalization without fine-tuning, outperforming Total3D, Im3D, and InstPIFu in both pose and shape metrics.
- The integration of scene context and relation modeling significantly enhances robustness against occlusion and clutter, setting a new state-of-the-art in holistic indoor scene reconstruction.
Significance
This work advances 3D scene understanding by combining generative diffusion models with scene priors, addressing longstanding issues like occlusion, noise, and inconsistent object arrangements. Its ability to produce globally coherent, detailed 3D reconstructions from a single image has profound implications for robotics, AR/VR, and content creation. The approach bridges the gap between 2D perception and 3D scene modeling, enabling applications that require high-fidelity, real-time scene understanding. Its robustness and generalization open new avenues for autonomous systems and immersive environments, pushing the frontier of single-view 3D reconstruction.
Technical Contribution
The paper proposes a novel diffusion-based framework for multi-object scene reconstruction, integrating a scene prior learned via intra-scene attention. It introduces an efficient surface alignment loss that samples points directly from intermediate shape representations, supporting training with partial labels. The architecture combines a U-Net for pose denoising, a Transformer for shape encoding, and a relation-aware attention module, enabling end-to-end joint pose and shape prediction. This approach surpasses prior regression and implicit methods in accuracy, detail, and scene consistency, establishing a new paradigm for generative 3D scene modeling.
Novelty
This is the first application of diffusion models to holistic indoor scene reconstruction from a single image, explicitly modeling inter-object relationships and jointly predicting poses and geometries. Unlike prior works limited to single objects or synthetic data, it leverages scene priors and relation-aware attention to produce coherent, detailed 3D scenes. The surface alignment loss further enables training without full supervision, marking a significant step forward in generative 3D modeling.
Limitations
- The model struggles with extreme occlusions and highly cluttered scenes, where object relationships are ambiguous. Computational costs remain high due to the diffusion process, limiting real-time deployment. Additionally, dynamic scenes and temporal consistency are not addressed, requiring future extensions for video or real-time applications.
Future Work
Future directions include integrating multi-modal data such as depth and semantics, optimizing for real-time inference, and extending to dynamic scenes with temporal coherence. Exploring unsupervised or weakly supervised training to reduce annotation dependency and scaling the approach for outdoor environments are also promising avenues.
AI Executive Summary
Reconstructing a complete 3D scene from a single RGB image remains a fundamental challenge in computer vision, especially in indoor environments with complex object arrangements and occlusions. Traditional methods often produce noisy, incomplete, or physically implausible results, limiting their practical applications in robotics, AR/VR, and content creation. Recent advances in deep learning have improved object-level reconstruction, but holistic scene understanding still faces hurdles in modeling inter-object relationships and scene coherence.
This paper introduces a novel diffusion-based approach that formulates the scene reconstruction task as a conditional generative process. By conditioning on the input image, the model jointly predicts the poses and geometries of all objects, leveraging a learned scene prior that captures contextual relationships. The core architecture combines a U-Net for pose denoising, a Transformer for shape encoding, and an intra-scene attention module to model object interactions. During training, a surface alignment loss based on point sampling from intermediate shape representations guides joint optimization, even with partial supervision.
Experimental results on SUN RGB-D and Pix3D datasets demonstrate significant improvements over state-of-the-art methods, with AP3D increasing by 12.04% and F-Score by 13.43%. The model produces more coherent, detailed, and accurate 3D reconstructions, effectively handling occlusions and clutter. Its ability to generalize across datasets without fine-tuning highlights its robustness and practical potential.
This work pushes the boundary of single-view 3D scene understanding, offering a generative framework that combines scene priors, relational modeling, and efficient training strategies. It opens new avenues for real-world applications in robotics, virtual reality, and digital content creation, setting a new standard for holistic scene reconstruction from minimal input.
Deep Dive
Plain Language Accessible to non-experts
想象你在一个工厂里，里面有很多不同的机器和工人。你只看了一眼照片，就想知道整个工厂的布局、每台机器的形状和位置。以前的方法就像用放大镜逐个看每个机器，费时又不准。而这篇论文介绍了一种聪明的“机器人画家”，它可以根据那一张照片，快速画出工厂里所有机器的3D模型，并且知道它们之间的关系，比如哪个机器在左边，哪个在右边，彼此不碰撞。这个机器人还会不断学习工厂的布局规律，确保画出来的场景既真实又合理。它用一种叫“扩散模型”的技术，像让画面逐渐变得清晰，最后能画出非常细腻的工厂全景。这样一来，不仅可以帮工厂规划，还能用在虚拟现实和游戏中，让虚拟场景变得和真实一样。
ELI14 Explained like you're 14
想象你在玩一个超级复杂的乐高拼图游戏，只看了一眼图片就要拼出整个场景。以前的方法就像用手慢慢拼，每次都拼错或漏掉细节。而科学家们发明了一种特别聪明的“魔法拼图”工具，它可以根据你给的那一张图片，自动帮你拼出完整的3D场景。这个工具就像一个会画画的机器人，它不仅知道每个乐高块应该放在哪里，还能理解这些块之间的关系，比如哪个块在上，哪个在旁边。它还会不断学习，变得越来越聪明，拼出来的场景既漂亮又符合逻辑。比如你看到一张房间的照片，它能帮你拼出里面的沙发、桌子、椅子，甚至连灯光和摆放都一模一样。这种技术未来可以用在虚拟现实、游戏设计，甚至帮机器人更好地理解周围的世界。是不是很酷？
Abstract
We present a novel diffusion-based approach for coherent 3D scene reconstruction from a single RGB image. Our method utilizes an image-conditioned 3D scene diffusion model to simultaneously denoise the 3D poses and geometries of all objects within the scene. Motivated by the ill-posed nature of the task and to obtain consistent scene reconstruction results, we learn a generative scene prior by conditioning on all scene objects simultaneously to capture the scene context and by allowing the model to learn inter-object relationships throughout the diffusion process. We further propose an efficient surface alignment loss to facilitate training even in the absence of full ground-truth annotation, which is common in publicly available datasets. This loss leverages an expressive shape representation, which enables direct point sampling from intermediate shape predictions. By framing the task of single RGB image 3D scene reconstruction as a conditional diffusion process, our approach surpasses current state-of-the-art methods, achieving a 12.04% improvement in AP3D on SUN RGB-D and a 13.43% increase in F-Score on Pix3D.