Uni3R：基于可泛化高斯泼溅的无位姿多视角图像统一三维重建与语义理解
Uni3R: Unified 3D Reconstruction and Semantic Understanding via Generalizable Gaussian Splatting from Unposed Multi-View Images
提出一种前馈式框架，直接从无位姿多视角图像联合重建带开放词表语义的 3D 高斯表示，可复用于需要同时进行新视角合成与开放词表 3D 语义分割的任务，避免逐场景优化。
原文依据 a novel feed-forward framework that jointly reconstructs a unified 3D scene representation enriched with open-vocabulary semantics, directly from unposed multi-view images
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稀疏二维视角重建并语义解释三维场景仍是计算机视觉中的一项基本挑战。传统方法通常将语义理解与重建解耦，或需要昂贵的逐场景优化，从而限制了其可扩展性与泛化能力。本文提出 Uni3R，一种新颖的前馈框架，可直接从无位姿多视角图像联合重建富含开放词表语义的统一三维场景表示。该方法利用跨视角 Transformer 稳健地整合任意多视角输入的信息，进而回归一组带有语义特征场的三维高斯基元。这种统一表示可在单次前馈过程中实现高保真新视角合成、开放词表三维语义分割和深度预测。大量实验表明，Uni3R 在多个基准上取得新的最优性能，包括域内数据集 RE10K 和 ScanNet，以及域外数据集 Mip-NeRF360。这项工作代表了迈向可泛化且统一的三维场景重建与理解的新范式。
英文原文摘要 1228 字符
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 sets a new state of the art across multiple benchmarks, including in-domain datasets such as RE10K and ScanNet, as well as the out-of-domain dataset Mip-NeRF360. This work represents a new paradigm toward generalizable and unified 3D scene reconstruction and understanding.