2020-01-21: Deep Depth Prior for Multi-View Stereo https://arxiv.org/abs/2001.07791v1We leverage the recently proposed idea of utilizing neural network as a prior for natural color image, and introduced three new loss terms that reconstruct clean and complete depth image
It was recently shown that the structure of convolutional neural networks
induces a strong prior favoring natural color images, a phenomena referred to
as a deep image prior (DIP), which can be an effective regularizer in inverse
problems such as image denoising, inpainting etc. In this paper, we investigate
a similar idea for depth images, which we call a deep depth prior.
Specifically, given a color image and a noisy and incomplete target depth map
from the same viewpoint, we optimize a randomly initialized CNN model to
reconstruct an RGB-D image where the depth channel gets restored by virtue of
using the network structure as a prior. We propose using deep depth priors for
refining and inpainting noisy depth maps within a multi-view stereo pipeline.
We optimize the network parameters to minimize two losses 1) a RGB-D
reconstruction loss based on the noisy depth map and 2) a multi-view
photoconsistency-based loss, which is computed using images from a
geometrically calibrated camera from nearby viewpoints. Our quantitative and
qualitative evaluation shows that our refined depth maps are more accurate and
complete, and after fusion, produces dense 3D models of higher quality.