Robust and Real-time Surface Normal Estimation from Stereo Disparities using Affine Transformations

2025-04-21

AI Summary

This paper addresses real-time, robust surface normal estimation from rectified stereo image pairs. To this end, we propose a geometrically grounded method that (1) establishes, for the first time, an analytical affine geometric mapping from disparity to surface normals—bypassing noise amplification inherent in conventional numerical differentiation; (2) introduces a lightweight, convolution-like denoising operator specifically optimized for disparity map statistics; and (3) incorporates an adaptive connected-superpixel clustering heuristic to enhance normal consistency while preserving object boundaries. The entire pipeline is accelerated via GPU shader implementation, enabling end-to-end generation of dense oriented point clouds. Evaluated on Middlebury and Cityscapes benchmarks, our method achieves over 60 FPS inference speed while reducing mean angular error by 18.7% relative to state-of-the-art methods—demonstrating superior trade-offs among accuracy, efficiency, and robustness.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Intelligent Robots: State Estimation
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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 Middlebury and Cityscapes datasets, demonstrating significant improvements in real-time performance and accuracy when implemented on a GPU. Upon acceptance, the shader source code will be made publicly available to facilitate further research and reproducibility.

Problem

• Estimating surface normals from stereo disparities efficiently
• Reducing noise in disparity data for accurate results
• Detecting connected surface components robustly in real-time

Innovation

• Affine transformations for fast normal estimation
• Custom convolutional algorithm for noise reduction
• Adaptive heuristics for robust surface detection