Laplacian ICP for Progressive Registration of 3D Human Head Meshes
2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition, FG 2023
We present a progressive 3D registration framework that is a highly-efficient variant of classical non-rigid Iterative Closest Points (N-ICP). Since it uses the Laplace-Beltrami operator for deformation regularisation, we view the overall process as Laplacian ICP (L-ICP). This exploits a 'small deformation per iteration' assumption and is progressively coarse-to-fine, employing an increasingly flexible deformation model, an increasing number of correspondence sets, and increasingly sophisticated correspondence estimation. Correspondence matching is only permitted within predefined vertex subsets derived from domain-specific feature extractors. Additionally, we present a new benchmark and a pair of evaluation metrics for 3D non-rigid registration, based on annotation transfer. We use this to evaluate our framework on a publicly-available dataset of 3D human head scans (Headspace). The method is robust and only requires a small fraction of the computation time compared to the most popular classical approach, yet has comparable registration performance.
Measurement, Three-dimensional displays, Laplace equations, Deformation, Annotations, Gesture recognition, Benchmark testing
N. Pears, H. Dai, W. Smith and H. Sun, "Laplacian ICP for Progressive Registration of 3D Human Head Meshes," 2023 IEEE 17th International Conference on Automatic Face and Gesture Recognition (FG), Waikoloa Beach, HI, USA, 2023, pp. 1-7, doi: 10.1109/FG57933.2023.10042743.