DA-AE: Disparity-Alleviation Auto-Encoder Towards Categorization of Heritage Images for Aggrandized 3D Reconstruction.

Dikshit Hegde, Tejas Anvekar, Ramesh Ashok Tabib, Uma Mudengudi; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 5093-5100

In this paper, we propose DA-AE: Disparity Alleviation AutoEncoder for categorization of heritage images towards 3D reconstruction. Recent survey on preservation of heritage shows demand for the digitization and conservations of heritage sites owing to their susceptibility to natural disasters and human acts. Digital conservation can be facilitated via crowdsourcing of data useful for construction of 3D models. Data from multiple sites sourced may result in elimination of relevant images due to the limitations of the pipeline. Curation and categorization of the crowdsourced data enables better 3D reconstruction. 3D reconstruction pipelines demand correlation between the data and also tries to eliminate the irrelevant information. The reconstruction pipeline is sensitive to selection of initial pair for reconstruction. By categorising individual sites, crowdsourced data can be used to create better 3D reconstructed models. Categorization of crowdsourced data demands learning robust representations of data. Towards this, we propose DA-AE for improved representation and categorization of data in latent space, along with a disparity alleviation loss. We demonstrate categorization as an event, with clustering as a downstream task. We compare our results of clustering with state-of-the-art methods on benchmark datasets (MNIST, FashionMNIST, and USPS). We demonstrate the effects of our categorization using custom dataset IDH10 and compare the results with state-of-the-art methods. We show a systematic and qualitative influence of the proposed method on 3D reconstruction of data.