从扩散视角重新思考视觉重排
Rethinking Visual Rearrangement from A Diffusion Perspective
将视觉重排建模为扩散桥过程，用点云输入和高斯混合分布表示物体状态，通过迭代去噪Transformer预测重排目标，为具身智能中的场景变化推理提供了新思路。
原文依据 we propose a novel perspective on the visual rearrangement task, drawing inspiration from the diffusion processes in molecular thermodynamics
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论将杂乱物体重新排列至其预期目标状态，要求智能体理解场景中已发生的变化并推理这些变化的过程。为此，我们受分子热力学中扩散过程的启发，为视觉重排任务提出了一种新颖的视角。我们将房间的乱序和恢复阶段建模为扩散的正向和逆向过程。与依赖场景建模和差分比较的传统方法不同，我们的方法深入洞察场景目标状态与初始状态之间的内在演化过程，从而通过细粒度且渐进的高置信度去噪步骤实现更合理的物体重排。通过分析任务目标，我们用物体的空间分布表示场景，并利用扩散桥模型对视觉重排过程进行建模。在此基础上，我们引入了扩散重排模型，该模型以点云数据为输入，将其拟合为高斯混合分布以表示物体状态，并通过迭代去噪Transformer预测重排目标。在RoomR数据集上的实验结果证明了我们方法的有效性。
英文原文摘要 1318 字符
Rearranging disarrayed objects to their intended goal states requires the agent to comprehend the changes that have occurred in the scene and to reason about the process of these changes. To address this, we propose a novel perspective on the visual rearrangement task, drawing inspiration from the diffusion processes in molecular thermodynamics. We model the room shuffle and unshuffle stages as the forward and reverse processes of diffusion. In contrast to conventional methods that rely on scene modeling and differential comparisons, our approach provides insight into the intrinsic evolution process between the goal and initial states of the scene, which allows for a more reasonable rearrangement of objects through fine-grained and progressive denoising steps with high confidence. By analyzing the task objectives, we represent the scene via spatial distributions of objects and model the visual rearrangement process using a diffusion bridge model. Building upon this, we introduce the Diffusion Rearrangement model, which takes point cloud data as input, fits it into Gaussian mixture distributions to represent the states of objects, and predicts the rearrangement target through an iterative denoising transformer. Experimental results on the RoomR dataset demonstrate the effectiveness of our approach.