Latent Uncertainty-Aware Multi-View SDF Scan Completion

Faezeh Zakeri, Lukas Ruppert, Raphael Braun, Hendrik P.A. Lensch; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2026, pp. 3556-3566

Imperfect reconstructions arising from occlusions, shadows, reflections, and other factors during 3D scanning often result in incomplete sections of the scanned object, with missing parts scattered randomly across its surface. We introduce an uncertainty-aware signed distance field (SDF) latent transformer that leverages uncertainty to identify and reconstruct missing parts based on the global shape of the incomplete scanned object and the immediate neighborhood of the affected regions. To our knowledge, we are the first to utilize uncertainties for SDF shape completion in the latent space. Our model has been trained on the entire Objaverse 1.0 dataset and demonstrates that our uncertainty-aware SDF completion method significantly outperforms previous works both numerically and visually. Code will be published at github.com/cgtuebingen/ua3dscancomp.