UniPR: Unified Object-level Real-to-Sim Perception and Reconstruction from a Single Stereo Pair

ArXi:2603.19616v1 Announce Type: new 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.