发表机构
Friedrich-Alexander University of Erlangen-Nürnberg (FAU)（埃尔朗根-纽伦堡弗里德里希·亚历山大大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对平坦无纹理表面在单光源下视差估计难题，提出SRDE算法，利用镜面反射几何特性而非纹理，在合成图像上端点误差改进超52%，并可与现有神经流程集成。
AI 中文摘要
多相机成像和相机阵列在自动驾驶、机器人控制或虚拟现实等许多应用中已变得无处不在，而物体及其环境的准确视差图对于可靠运行至关重要。尽管神经网络最近取得了进展，但正确估计平坦且无纹理物体的视差仍然具有挑战性。特别地，我们考虑一个由单个固定光源照明的平坦、无纹理表面所定义的场景，导致物体表面出现一个主要的镜面反射。在这些条件下，可靠的几何和光度线索缺失，镜面反射常常导致错误预测，尤其是当使用依赖纹理信息来匹配对应像素的传统方法时。为了解决这个问题，我们引入了新颖的镜面反射视差估计（SRDE）算法，该算法专门针对平面、无纹理物体和单光源照明的受限场景而设计。与传统立体匹配方法不同，SRDE忽略纹理，而是利用镜面反射的几何特性，结合反射区域、光源和相机设置的位置信息。我们表明，SRDE以显著优势超越了现有方法，在合成图像上端点误差实现了超过52%的改进。进一步测试证明了其在真实世界数据上的优越性能。此外，我们将SRDE集成到现有的神经视差估计流程中，通过选择性地替换镜面反射区域的预测，而无需修改骨干模型。这种混合策略无需网络重新训练即可获得额外的性能提升。
英文摘要
Multi-camera imaging and camera arrays have become ubiquitous in many applications, such as autonomous driving, robot control, or virtual reality, and accurate disparity maps of objects and their environment are essential for reliable operation. Despite recent advances in neural networks, correctly estimating the disparity of flat and textureless objects remains challenging. In particular, we consider a scenario defined by flat, textureless surfaces illuminated by a single fixed light source, resulting in one dominant specular reflection visible on the object surface. Under these conditions, reliable geometric and photometric cues are missing, and the specular reflection often causes mispredictions, especially when using conventional methods that rely on texture information to match corresponding pixels. To address this issue, the novel Specular Reflection Disparity Estimation SRDE algorithm is introduced, which is specifically designed for the constrained scenario of planar, textureless objects and single-source illumination. Unlike conventional stereo matching methods, SRDE ignores texture and instead leverages the geometric properties of specular reflections by incorporating the position information of the reflective region, the light source, and the camera setup. We show that SRDE outperforms existing methods by a notable margin, achieving more than a 52% improvement in End Point Error on synthetic images. Further tests demonstrate superior performance on real-world data. Furthermore, we integrate SRDE into existing neural disparity estimation pipelines by selectively replacing predictions in specular regions without modifying the backbone model. This hybrid strategy enables additional performance gains without requiring network retraining.
Journal refIEEE Transactions on Instrumentation and Measurement, Vol. 75, 2026
