Topologically Consistent Multi-view 3D Head Reconstruction via Coarse-Guided Layered Surface Sampling

Abstract

We present SHELLS (Semantic Head Estimation via Layered Local Sampling), an efficient feed-forward framework for 3D head reconstruction in dense semantic correspondence from multi-view images. State-of-the-art methods typically refine vertices independently using localized feature volumes. This couples memory-intensive feature sampling to the output mesh resolution, limiting scalability for dense topologies (≥ 10k vertices) and producing noisy surfaces due to a lack of global context. In contrast, SHELLS decouples feature extraction from mesh resolution via a hierarchical sampling strategy. We extract multi-view features using a DinoV2 backbone with LoRA adaptation, projectively sample a sparse global feature cloud, and predict an intermediate coarse mesh. This coarse prior guides the construction of layered, surface-aware sampling shells that serve as a discrete search space for the final reconstruction. By utilizing a shared transformer architecture, SHELLS maintains global surface consistency while requiring only 12% of the GPU memory of volume-based approaches. Experimental results show that SHELLS reduces median registration error by 21% – 29% and achieves a 3.5× speedup, predicting 18k-vertex meshes in 0.08 seconds. Notably, our model is trained exclusively on synthetic data yet generalizes effectively to real-world captures, eliminating the need for the costly, pre-registered multi-view datasets common in prior work.

 @inproceedings{Bolkart2026SHELLS, author    = {Bolkart, Timo and Wang, Daoye and Chandran, Prashanth}, title     = {Topologically Consistent Multi-view 3D Head Reconstruction via Coarse-Guided Layered Surface Sampling}, year      = {2026}, publisher = {Association for Computing Machinery}, keywords  = {Registration, 3D Head Reconstruction}, series    = {SIGGRAPH Conference Papers '26} }