A Comparative Evaluation of Geometric Accuracy in NeRF and Gaussian Splatting

2026-04-20

AI Summary

Existing neural rendering methods are predominantly evaluated based on image visual quality, often overlooking geometric surface accuracy—a critical requirement for applications such as robotic grasping. To address this gap, this work proposes the first systematic evaluation framework specifically designed to assess the geometric fidelity of neural rendering techniques. Leveraging mainstream approaches including NeRF and Gaussian Splatting, the study evaluates surface and shape accuracy across 19 diverse 3D scenes using established geometric reconstruction metrics. By establishing a reproducible benchmark for geometric precision, this framework complements conventional visual-quality assessments and provides a reliable foundation for selecting and optimizing neural rendering methods in high-accuracy applications.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• geometric accuracy
• NeRF
• Gaussian Splatting
• surface fidelity
• neural rendering

Innovation

• geometric accuracy
• neural rendering
• evaluation benchmark
• surface fidelity
• 3D scene representation