Multi-view 3D surface reconstruction using neural implicit representations has made notable progress by modeling the geometry and view-dependent radiance fields within a unified framework. However, their effectiveness in reconstructing objects with specular or complex surfaces is typically biased by the directional parameterization used in their view-dependent radiance network. {\it Viewing direction} and {\it reflection direction} are the two most commonly used directional parameterizations but have their own limitations. Typically, utilizing the viewing direction usually struggles to correctly decouple the geometry and appearance of objects with highly specular surfaces, while using the reflection direction tends to yield overly smooth reconstructions for concave or complex structures. In this paper, we analyze their failed cases in detail and propose a novel hybrid directional parameterization to address their limitations in a unified form. Extensive experiments demonstrate the proposed hybrid directional parameterization consistently delivered satisfactory results in reconstructing objects with a wide variety of materials, geometry and appearance, whereas using other directional parameterizations faces challenges in reconstructing certain objects. Moreover, the proposed hybrid directional parameterization is nearly parameter-free and can be effortlessly applied in any existing neural surface reconstruction method.

本研究解决了神经隐式表示的多视角3D表面重建中，传统方向参数化（视角方向和反射方向）在重建复杂表面时的不足。提出了一种新型混合方向参数化方法，该方法有效改善了对不同材质和几何形状物体的重建效果，实验结果表明其在性能上优于现有方法，且几乎无参数，易于集成到现有神经表面重建技术中。

重新思考神经隐式表面重建中的方向参数化