Fast Active Reconstruction by Omnidirectional View

In this work, we explore panoramic vision for fast active reconstruction, leveraging its omnidirectional observations to enable efficient UAV-based 3D scanning beyond existing perspective-based methods that require frequent camera adjustments due to their limited fields of view. Specifically, we propose FAR, a closed-loop perception–planning–capture framework with two key components. A Spherical Ray Adapter (SRA) incorporates synchronized camera poses into perspective-based VGGT-Ω, enabling streaming metric panoramic depth prediction with cross-window consistency. Adaptive Initialization automatically configures planning parameters from the scene scale for efficient exploration across diverse environments. To support systematic evaluation, we further develop a unified panoramic simulation platform with real-time point-cloud output and standardized planner interfaces, and construct a large-scale benchmark with panoramic observations, accurate poses, and ground-truth geometry. Extensive experiments show that FAR outperforms existing methods in both planning and reconstruction; with RGB panoramas only, it reaches 83.3% coverage with a 1017 m mean path, exceeding MAGICIAN with ground-truth depth (79.4%, 1400 m). The proposed simulator, benchmark, and models will be publicly released.