SAIL-Recon is a feed-forward Transformer-based system for large-scale Structure-from-Motion (SfM) and visual localization. It processes input images or video to estimate camera poses, depth maps, and generate 3D point clouds without iterative optimization. The architecture combines a Vision Transformer backbone with an alternating attention aggregator for temporal modeling and specialized heads for camera, depth, and track predictions. The system includes both command-line and Gradio-based demo interfaces for end-to-end 3D reconstruction, along with evaluation scripts for standard benchmarks (TUM, 7Scenes, Tank&Temple, etc.). While research-oriented, it is packaged as a usable application with clear entry points and modular components.
CameraBench is a benchmark and research project for evaluating camera motion understanding in videos, comparing classical Structure-from-Motion (SfM) methods and modern Vision-Language Models (VLMs). The repository serves as a landing page with download scripts for the test dataset (videos and labels from Hugging Face), a leaderboard showing model performance, and links to fine-tuned Qwen2.5-VL models. The actual evaluation and training code resides in a separate repository (t2v_metrics). The repo lacks a self-contained pipeline, dependency management, or data processing scripts; it is essentially a static site with download helpers. A notebook template for a ReLU activation function exercise is included but is unrelated to the core project. The benchmark is novel and published at NeurIPS 2025, but the codebase itself is minimal and not reusable beyond data retrieval.