标题： MapAnything：通用前馈度量3D重建 显示英文标题

标题： MapAnything: Universal Feed-Forward Metric 3D Reconstruction

摘要： 我们引入了MapAnything，这是一个统一的基于Transformer的前馈模型，它能够接收一个或多个图像以及可选的几何输入，如相机内参、位姿、深度或部分重建结果，然后直接回归度量3D场景几何和相机。 MapAnything利用多视角场景几何的分解表示，即深度图、局部光线图、相机位姿和度量尺度因子的集合，这有效地将局部重建升级为全局一致的度量框架。 在不同数据集上标准化监督和训练，并结合灵活的输入增强，使MapAnything能够在一次前馈传递中解决广泛的3D视觉任务，包括未校准的运动恢复结构、校准的多视图立体视觉、单目深度估计、相机定位、深度补全等。 我们提供了广泛的实验分析和模型消融实验，证明MapAnything在性能上优于或等于专业前馈模型，同时提供更高效的联合训练行为，从而为通用的3D重建主干网络铺平道路。 显示英文摘要

摘要： We introduce MapAnything, a unified transformer-based feed-forward model that ingests one or more images along with optional geometric inputs such as camera intrinsics, poses, depth, or partial reconstructions, and then directly regresses the metric 3D scene geometry and cameras. MapAnything leverages a factored representation of multi-view scene geometry, i.e., a collection of depth maps, local ray maps, camera poses, and a metric scale factor that effectively upgrades local reconstructions into a globally consistent metric frame. Standardizing the supervision and training across diverse datasets, along with flexible input augmentation, enables MapAnything to address a broad range of 3D vision tasks in a single feed-forward pass, including uncalibrated structure-from-motion, calibrated multi-view stereo, monocular depth estimation, camera localization, depth completion, and more. We provide extensive experimental analyses and model ablations demonstrating that MapAnything outperforms or matches specialist feed-forward models while offering more efficient joint training behavior, thus paving the way toward a universal 3D reconstruction backbone.