Core Insights - The article discusses the SAIL-Recon framework, which integrates scene regression with localization to achieve large-scale Structure from Motion (SfM) using thousands of images efficiently and accurately [7][10][34]. Group 1: Traditional SfM Limitations - Traditional SfM algorithms rely on feature extraction, matching, triangulation, and bundle adjustment, which can fail in low-texture, blurry, or repetitive texture scenes [5]. - Recent research has proposed an end-to-end learnable SfM pipeline that directly regresses scene structure and camera poses from images, but it is limited by GPU memory when handling large-scale scenes [5][10]. Group 2: SAIL-Recon Framework - SAIL-Recon is a multi-task framework that unifies reconstruction and localization without the need for scene-specific training, sampling a few anchor images from large image or video sequences to infer neural scene representations [7][10]. - The framework achieves state-of-the-art (SOTA) performance across multiple benchmarks, surpassing both traditional and learning-based methods in accuracy and efficiency [10][34]. Group 3: Methodology - The SAIL-Recon process involves selecting a small number of anchor images to extract neural scene representations, which are then used to jointly estimate scene coordinates and camera poses for all images [9][10]. - The method employs a transformer to compute scene representations and camera parameters, optimizing GPU memory usage through a key-value cache [11][12]. Group 4: Experimental Results - SAIL-Recon demonstrated superior performance in pose estimation and new view synthesis tasks, achieving the highest PSNR in the Tanks & Temples dataset and completing reconstructions significantly faster than traditional methods [26][32]. - The framework maintains good performance even when reducing the number of anchor images from 10 to 2, indicating robustness in various sampling strategies [32]. Group 5: Limitations and Future Work - The framework's reliance on a fixed global coordinate system may affect certain sequences, suggesting a need for improved anchor image selection strategies [36]. - Uniform sampling could overlook scene areas, indicating potential for research into coverage-aware sampling methods [36].