无需3D重建即可精准校准径向畸变，提升复杂场景下的相机标定效率。
PRaDA: Projective Radial Distortion Averaging
- 在投影空间中解耦畸变参数与3D结构，避免全量SfM计算
- 通过成对投影关系平均多组畸变估计，精度媲美传统SfM方法
- 兼容任意特征匹配方式，无需跨图像点轨迹构建
我们针对挑战性条件下径向畸变相机的自动标定问题提出新方法。传统方法需解决完整的结构从运动（SfM）问题，依赖大量重叠图像，或依赖学习类方法但精度不足。本文证明畸变标定可脱离3D重建，在保持SfM高精度的同时规避其复杂性。核心思想是利用投影空间中几何仅受单应性约束的特性，将除畸变外所有相机参数封装于单应性中。所提方法——投影径向畸变平均（PRaDA），在完全投影框架下，基于成对投影关系直接平均多个畸变估计，无需生成3D点或进行完整束调整。该方法支持任意特征匹配策略，且无需构建跨图像点轨迹。
原文摘要 · Abstract (English)
We tackle the problem of automatic calibration of radially distorted cameras in challenging conditions. Accurately determining distortion parameters typically requires either 1) solving the full Structure from Motion (SfM) problem involving camera poses, 3D points, and the distortion parameters, which is only possible if many images with sufficient overlap are provided, or 2) relying heavily on learning-based methods that are comparatively less accurate. In this work, we demonstrate that distortion calibration can be decoupled from 3D reconstruction, maintaining the accuracy of SfM-based methods while avoiding many of the associated complexities. This is achieved by working in Projective Space, where the geometry is unique up to a homography, which encapsulates all camera parameters except for distortion. Our proposed method, Projective Radial Distortion Averaging, averages multiple distortion estimates in a fully projective framework without creating 3d points and full bundle adjustment. By relying on pairwise projective relations, our methods support any feature-matching approaches without constructing point tracks across multiple images.
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