High Fidelity 3D Reconstructions
with Limited Physical Views

1Carnegie Mellon University, 2Apple, 3The University of Adelaide

International Conference on 3D Vision - 3DV, 2021

Overview of Neural Prior based MV-NRSfM

Enforcing Neural "shape" Priors and multi-view equivariance within modern deep 2D-3D lifting enables generation of high-fidelity 3D reconstructions using just 2-3 uncalibrated cameras, compared to >100 calibrated cameras.


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 in order to obtain the high fidelity 3D reconstructions necessary for many modern applications. In this paper we present a novel approach that leverages recent advances in 2D-3D lifting using neural shape priors while also enforcing multi-view equivariance - and term this approach Multi-view Non-Rigid Structure-from-Motion (MV-NRSfM). We show how our method can achieve comparable fidelity to expensive calibrated multi-view rigs using a limited (2-3) number of uncalibrated camera views.

Deep-dive video

More Results

We show results and baseline comparisons on three datasets. For all the datasets, we compare against triangulation that uses a substantially large (~ 30-50 for monkey dataset) number of calibrated camera views. We also compare against the single view NRSfM versus multi-view NRSfM (ours). Please see the paper for more details.


  author      = {Dabhi, Mosam and Wang, Chaoyang and Saluja, Kunal and Jeni, Laszlo and Fasel, Ian and Lucey, Simon},
  title       = {High Fidelity 3D Reconstructions with Limited Physical Views},
  booktitle   = {International Conference on 3D Vision (3DV)},
  year        = {2021},