Explaining Human Preferences via Metrics for Structured 3D Reconstruction

Jack Langerman, Denys Rozumnyi, Yuzhong Huang, Dmytro Mishkin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 26944-26953

Abstract

"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.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Langerman_2025_ICCV, author = {Langerman, Jack and Rozumnyi, Denys and Huang, Yuzhong and Mishkin, Dmytro}, title = {Explaining Human Preferences via Metrics for Structured 3D Reconstruction}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2025}, pages = {26944-26953} }