Uni3R：通过通用高斯喷射从无序多视角图像实现统一的3D重建与语义理解
Uni3R: Unified 3D Reconstruction and Semantic Understanding via Generalizable Gaussian Splatting from Unposed Multi-View Images
AI总结：
本文提出Uni3R框架，通过统一的3D场景表示实现高保真视角合成和开放词汇语义分割，展示了其在多个基准上的优越性能。
摘要：
本文提出Uni3R框架，通过统一的3D场景表示实现高保真视角合成和开放词汇语义分割，展示了其在多个基准上的优越性能。
英文摘要：
Reconstructing and semantically interpreting 3D scenes from sparse 2D views remains a fundamental challenge in computer vision. Conventional methods often decouple semantic understanding from reconstruction or necessitate costly per-scene optimization, thereby restricting their scalability and generalizability. In this paper, we introduce Uni3R, a novel feed-forward framework that jointly reconstructs a unified 3D scene representation enriched with open-vocabulary semantics, directly from unposed multi-view images. Our approach leverages a Cross-View Transformer to robustly integrate information across arbitrary multi-view inputs, which then regresses a set of 3D Gaussian primitives endowed with semantic feature fields. This unified representation facilitates high-fidelity novel view synthesis, open-vocabulary 3D semantic segmentation, and depth prediction, all within a single, feed-forward pass. Extensive experiments demonstrate that Uni3R establishes a new state-of-the-art across multiple benchmarks, including 25.07 PSNR on RE10K and 55.84 mIoU on ScanNet. Our work signifies a novel paradigm towards generalizable, unified 3D scene reconstruction and understanding. The code is available at https://github.com/HorizonRobotics/Uni3R.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
