DA-AE: Disparity-Alleviation Auto-Encoder Towards Categorization of Heritage Images for Aggrandized
Channel: Dmytro Mishkin
Date: 2022-06-20

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

Dikshit Hegde, Tejas Anvekar, Ramesh Ashok Tabib, Uma Mudengudi Center of Excellence in Visual Intelligence (CEVI), KLE Technological University. Vidya Nagar, Hubballi, Karnakata, India.

In this paper, we propose DA-AE: Disparity Alleviation AutoEncoder for categorization of heritage images towards 3D reconstruction. Recent survey on preservation of her- itage shows demand for the digitization and conservations of heritage sites owing to their susceptibility to natural dis- asters and human acts. Digital conservation can be facil- itated 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 crowd- sourced data enables better 3D reconstruction. 3D recon- struction 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 recon- structed models. Categorization of crowdsourced data de- mands learning robust representations of data. Towards this, we propose DA-AE for improved representation and categorization of data in latent space, along with a dispar- ity 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 cus- tom dataset IDH10 and compare the results with state-of- the-art methods. We show a systematic and qualitative in- fluence of the proposed method on 3D reconstruction of data.

CVPR2022 Image Matching workshop.