3D Reconstruction of Sculptures from Single Images via Unsupervised Domain Adaptation on Implicit Models
This addresses the challenge of creating virtual exhibits for VR museums with limited data and domain shift, though it is incremental as it adapts existing methods to a new domain.
The paper tackles the problem of 3D reconstruction of sculptures from single images for VR museums by proposing an unsupervised domain adaptation method to adapt a model from real-world humans to sculptures, achieving effectiveness demonstrated through comparisons, ablation studies, and a user study.
Acquiring the virtual equivalent of exhibits, such as sculptures, in virtual reality (VR) museums, can be labour-intensive and sometimes infeasible. Deep learning based 3D reconstruction approaches allow us to recover 3D shapes from 2D observations, among which single-view-based approaches can reduce the need for human intervention and specialised equipment in acquiring 3D sculptures for VR museums. However, there exist two challenges when attempting to use the well-researched human reconstruction methods: limited data availability and domain shift. Considering sculptures are usually related to humans, we propose our unsupervised 3D domain adaptation method for adapting a single-view 3D implicit reconstruction model from the source (real-world humans) to the target (sculptures) domain. We have compared the generated shapes with other methods and conducted ablation studies as well as a user study to demonstrate the effectiveness of our adaptation method. We also deploy our results in a VR application.