Robust and Real-Time Surface Normal Estimation from Stereo Disparities Using Affine Transformations

Abstract

This work introduces a novel method for surface normal estimation from rectified stereo image pairs, leveraging affine transformations derived from disparity values to achieve fast and accurate results. We demonstrate how the rectification of stereo image pairs simplifies the process of surface normal estimation by reducing computational complexity. To address noise reduction, we develop a custom algorithm inspired by convolutional operations, tailored to process disparity data efficiently. We also introduce adaptive heuristic techniques for efficiently detecting connected surface components within the images, further improving the robustness of the method. By integrating these methods, we construct a surface normal estimator that is both fast and accurate, producing a dense, oriented point cloud as the final output. Our method is validated using both simulated environments and real-world stereo images from the Middleburyfootnote{For the Middlebury datasets, disparity values are published.} and Cityscapes datasets, demonstrating significant improvements in real-time performance and accuracy when implemented on a GPU. The source code is available at https://github.com/mrafifaisal/Surface-Normal-Estimation/.