3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation

TL;DR

This paper introduces a modular toolkit for 3D face reconstruction error measurement, enabling fair, fast, and detailed evaluation of different methods and estimators, with a focus on improving benchmarking accuracy and efficiency.

Contribution

The authors present M3DFB, a modular, interchangeable benchmark toolkit for 3D face reconstruction error analysis, including a novel correction component and a comprehensive evaluation of error estimators.

Findings

ICP-based estimator performs poorly in ranking accuracy.

Non-rigid alignment significantly improves error correlation.

Proposed correction scheme achieves high accuracy and faster performance.

Abstract

Computing the standard benchmark metric for 3D face reconstruction, namely geometric error, requires a number of steps, such as mesh cropping, rigid alignment, or point correspondence. Current benchmark tools are monolithic (they implement a specific combination of these steps), even though there is no consensus on the best way to measure error. We present a toolkit for a Modularized 3D Face reconstruction Benchmark (M3DFB), where the fundamental components of error computation are segregated and interchangeable, allowing one to quantify the effect of each. Furthermore, we propose a new component, namely correction, and present a computationally efficient approach that penalizes for mesh topology inconsistency. Using this toolkit, we test 16 error estimators with 10 reconstruction methods on two real and two synthetic datasets. Critically, the widely used ICP-based estimator provides the worst benchmarking performance, as it significantly alters the true ranking of the top-5 reconstruction methods. Notably, the correlation of ICP with the true error can be as low as 0.41. Moreover, non-rigid alignment leads to significant improvement (correlation larger than 0.90), highlighting the importance of annotating 3D landmarks on datasets. Finally, the proposed correction scheme, together with non-rigid warping, leads to an accuracy on a par with the best non-rigid ICP-based estimators, but runs an order of magnitude faster. Our open-source codebase is designed for researchers to easily compare alternatives for each component, thus helping accelerating progress in benchmarking for 3D face reconstruction and, furthermore, supporting the improvement of learned reconstruction methods, which depend on accurate error estimation for effective training.