What Makes a 3D Scene Editable? A Factorized Benchmark of Fidelity, Locality, Consistency, and Preservation

2026-09-13

AI Summary

研究通过引入EditBench3D评估3D场景编辑的四个属性：保真度、局部性、一致性和非目标内容保留，使用多种编辑方法并揭示了各方法在不同维度上的表现差异。

Abstract

Neural 3D scene editing is often evaluated by semantic alignment alone, although a convincing result may alter unrelated content or become inconsistent across views. We introduce EditBench3D, a representation-agnostic benchmark that treats editing as controlled information replacement. It evaluates four complementary properties: instruction fidelity, spatial locality, cross-view consistency, and preservation of non-target content. The protocol combines visibility-aware 3D target supports, paired descriptions, held-out cameras, and five edit families covering appearance, material, geometry, and object-level changes. We evaluate eight representative NeRF, 3D Gaussian Splatting, hybrid, and proxy-based editors on 240 scene-edit pairs. The study shows that semantic fidelity is only weakly associated with the other editing properties, and that no single method is optimal across all dimensions. Explicit Gaussian editors offer a strong overall balance, whereas direct proxy manipulation provides the most conservative edits at the cost of open-ended fidelity. These findings support reporting editability as a multi-objective profile rather than a single semantic score.

Problem

• 3D Scene Editing
• Semantic Alignment
• Evaluation Benchmark
• Cross-View Consistency
• Content Preservation

Innovation

• EditBench3D
• multi-objective profile
• spatial locality
• cross-view consistency
• preservation