对比NeRF与高斯溅射在几何精度上的表现，填补视觉质量评估的空白。
A Comparative Evaluation of Geometric Accuracy in NeRF and Gaussian Splatting
- 构建针对几何精度的评估流程，聚焦表面与形状保真度。
- 使用19个多样化场景构成基准，系统评测重建方法。
- 适用于机器人抓取等需要精确几何信息的任务场景。
神经渲染技术的进展催生了多种3D场景表示方法。尽管传统计算机视觉指标可评估生成图像的视觉质量，却常忽略表面几何的保真度。这一缺陷在机器人领域尤为关键，因精准几何对抓取和物体操作至关重要。本文提出一种专注于几何精度的神经渲染方法评估流程，并构建包含19个多样化场景的基准数据集。该方法能系统评估重建算法在表面与形状保真度方面的表现，补充了传统的视觉质量指标。
原文摘要 · Abstract (English)
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.
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