用专家偏好验证3D重建评估指标，提出可适配场景的推荐方案。
Explaining Human Preferences via Metrics for Structured 3D Reconstruction
- 基于专家模型师偏好设计系统性验证测试
- 发现传统指标在结构化3D重建中存在明显缺陷
- 提出基于人类判断学习的改进评估指标，适合专业设计场景
本文深入探讨了结构化3D重建的自动化评估指标。分析了各类指标的潜在缺陷，并通过专家3D建模师的偏好进行验证。提出了系统化的‘单元测试’来实证评估指标的理想特性，并根据应用场景提供指标选用建议。最后，提出一种从人类专家判断中提炼出的可学习评估指标并进行分析。源代码已开源。
原文摘要 · Abstract (English)
"What cannot be measured cannot be improved" while likely never uttered by Lord Kelvin, summarizes effectively the driving force behind this work. This paper presents a detailed discussion of automated metrics for evaluating structured 3D reconstructions. Pitfalls of each metric are discussed, and an analysis through the lens of expert 3D modelers' preferences is presented. A set of systematic "unit tests" are proposed to empirically verify desirable properties, and context aware recommendations regarding which metric to use depending on application are provided. Finally, a learned metric distilled from human expert judgments is proposed and analyzed. The source code is available at https://github.com/s23dr/wireframe-metrics-iccv2025
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