Recent advances in neural rendering have introduced numerous 3D scene representations. Although standard computer vision metrics evaluate the visual quality of generated images, they often overlook the fidelity of surface geometry. This limitation is particularly critical in robotics, where accurate geometry is essential for tasks such as grasping and object manipulation. In this paper, we present an evaluation pipeline for neural rendering methods that focuses on geometric accuracy, along with a benchmark comprising 19 diverse scenes. Our approach enables a systematic assessment of reconstruction methods in terms of surface and shape fidelity, complementing traditional visual metrics.

翻译：神经渲染领域的最新进展催生了多种三维场景表示方法。尽管标准计算机视觉指标评估生成图像的视觉质量，但往往忽视了表面几何的保真度。这一局限性在机器人领域尤为关键，因为精确的几何结构对抓取和物体操控等任务至关重要。本文提出一种聚焦于几何精度的神经渲染方法评估流程，并配套构建包含19个多样化场景的基准数据集。该方法实现了从表面与形状保真度层面系统评估重建技术，有效补充传统视觉指标。