UniPR：基于单对立体图像的统一物体级真实到仿真感知与重建
UniPR: Unified Object-level Real-to-Sim Perception and Reconstruction from a Single Stereo Pair
将检测、分割、形状重建与姿态估计统一为单次前向的端到端框架，并借助双目几何约束解决尺度歧义；其 Pose-Aware Shape Representation 把姿态信息编码进形状表示，可作为物体级 real-to-sim 与机器人仿真数据生成的通用范式。
原文依据 the first end-to-end object-level real-to-sim perception and reconstruction framework
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从图像中感知并重建物体对于真实到仿真迁移任务至关重要，这类任务在机器人领域被广泛使用。现有方法依赖检测、分割、形状重建和姿态估计等多个子模块来完成整个流程。然而，这种模块化流程存在效率低下和误差累积的问题，因为每个阶段只处理部分信息或局部精化的信息，同时丢弃了全局上下文。为解决这些局限，我们提出 UniPR，这是首个端到端的物体级真实到仿真感知与重建框架。UniPR 直接作用于单对立体图像，利用几何约束解决尺度歧义。我们引入姿态感知形状表示，从而无需为每个类别定义规范形状，并弥合了重建与姿态估计任务之间的鸿沟。此外，我们构建了一个大规模词汇的立体数据集 LVS6D，包含超过 6,300 个物体，以促进该领域的大规模研究。大量实验表明，UniPR 在单次前向传播中并行重建场景中的所有物体，实现了显著的效率提升，并在多种物体类型上保持了真实的物理比例，凸显了其在实际机器人应用中的潜力。
英文原文摘要 1339 字符
Perceiving and reconstructing objects from images are critical for real-to-sim transfer tasks, which are widely used in the robotics community.Existing methods rely on multiple submodules such as detection, segmentation, shape reconstruction, and pose estimation to complete the pipeline.However, such modular pipelines suffer from inefficiency and cumulative error, as each stage operates on only partial or locally refined information while discarding global context.To address these limitations, we propose UniPR, the first end-to-end object-level real-to-sim perception and reconstruction framework.Operating directly on a single stereo image pair, UniPR leverages geometric constraints to resolve the scale ambiguity.We introduce Pose-Aware Shape Representation to eliminate the need for per-category canonical definitions and to bridge the gap between reconstruction and pose estimation tasks.Furthermore, we construct a large-vocabulary stereo dataset, LVS6D, comprising over 6,300 objects, to facilitate large-scale research in this area.Extensive experiments demonstrate that UniPR reconstructs all objects in a scene in parallel within a single forward pass, achieving significant efficiency gains and preserves true physical proportions across diverse object types, highlighting its potential for practical robotic applications.