High Fidelity 3D Reconstructions with Limited Physical Views (3DV 2021) | Full Talk.
Channel: Mosam Dabhi
Date: 30/11/2021

This is the full talk video for our work where we demonstrate that it is possible to generate high fidelity 3D reconstructions using as little as two or three uncalibrated physical views and still achieve comparable fidelity to complex multi-view rigs. This work is set to appear in the 3DV 2021 conference (https://3dv2021.surrey.ac.uk). For a shorter introduction video, please visit the following: https://youtu.be/Jx7mJ_D88gg

Abstract: Multi-view triangulation is the gold standard for 3D reconstruction from 2D correspondences, given known calibration and sufficient views. However in practice expensive multi-view setups — involving tens sometimes hundreds of cameras — are required to obtain the high fidelity 3D reconstructions necessary for modern applications. In this work we present a novel approach that leverages recent advances in 2D-3D lifting using neural shape priors while also enforcing multi-view equivariance. We show that our method can achieve comparable fidelity to expensive calibrated multi-view rigs using a limited (2-3) number of uncalibrated camera views.

Paper link: https://arxiv.org/pdf/2110.11599.pdf
Project description: https://machinelearning.apple.com/research/high-fidelity-3d-reconstructions
Project webpage: https://sites.google.com/view/high-fidelity-3d-neural-prior
2 min. intro: https://youtu.be/Jx7mJ_D88gg