用仿射变换加速立体图像法向量估计，实时准确。
Robust and Real-time Surface Normal Estimation from Stereo Disparities using Affine Transformations
- 基于视差的仿射变换简化法向量计算
- 在GPU上实现毫秒级处理，精度显著提升
- 适合实时三维重建与机器人感知场景
本文提出一种从校正后的立体图像对中估计表面法向量的新方法，利用视差值推导的仿射变换实现快速精准的结果。通过立体图像校正，大幅降低法向量估计的计算复杂度。为抑制噪声，设计了类卷积操作的定制算法，高效处理视差数据；同时引入自适应启发式策略，有效检测图像中连通表面区域，进一步增强鲁棒性。集成上述方法后，构建出兼具速度与精度的法向量估计算法，输出稠密且带方向的点云。在模拟环境及真实数据集（Middlebury、Cityscapes）上验证，该方法在GPU上实现了显著的实时性能与高精度。论文被接受后，将公开着色器源码，以促进研究复现。
原文摘要 · Abstract (English)
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.
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