# Dense Depth Estimation from Multiple 360-degree Images Using Virtual Depth

arXiv: https://arxiv.org/abs/2112.14931 · HTML: https://mlsanity.com/paper/2112.14931/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 53 references that are in the map.
- First posted on arXiv 30 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [GA-Net: Guided Aggregation Net for End-to-end Stereo Matching](https://mlsanity.com/paper/1904.06587/) (2019)
- [D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry](https://mlsanity.com/paper/2003.01060/) (2020)
- [UniFuse: Unidirectional Fusion for 360circ Panorama Depth Estimation](https://mlsanity.com/paper/2102.03550/) (2021)
- [The Fast Bilateral Solver](https://mlsanity.com/paper/1511.03296/) (2015)
- [DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama](https://mlsanity.com/paper/1811.11977/) (2018)
- [Visual Odometry Revisited: What Should Be Learnt?](https://mlsanity.com/paper/1909.09803/) (2019)
- [Corners for Layout: End-to-End Layout Recovery from 360 Images](https://mlsanity.com/paper/1903.08094/) (2019)
- [Spherical View Synthesis for Self-Supervised 360 Depth Estimation](https://mlsanity.com/paper/1909.08112/) (2019)
- [360SD-Net: 360deg Stereo Depth Estimation with Learnable Cost Volume](https://mlsanity.com/paper/1911.04460/) (2019)
- [Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters](https://mlsanity.com/paper/1811.10863/) (2018)
- [FADNet: A Fast and Accurate Network for Disparity Estimation](https://mlsanity.com/paper/2003.10758/) (2020)
- [Beyond Tracking: Selecting Memory and Refining Poses for Deep Visual Odometry](https://mlsanity.com/paper/1904.01892/) (2019)
- [Drone Detection Using Depth Maps](https://mlsanity.com/paper/1808.00259/) (2018)
- [3D Reconstruction from Full-view Fisheye Camera](https://mlsanity.com/paper/1506.06273/) (2015)

## Abstract (arXiv)

In this paper, we propose a dense depth estimation pipeline for multiview 360deg images. The proposed pipeline leverages a spherical camera model that compensates for radial distortion in 360deg images. The key contribution of this paper is the extension of a spherical camera model to multiview by introducing a translation scaling scheme. Moreover, we propose an effective dense depth estimation method by setting virtual depth and minimizing photonic reprojection error. We validate the performance of the proposed pipeline using the images of natural scenes as well as the synthesized dataset for quantitive evaluation. The experimental results verify that the proposed pipeline improves estimation accuracy compared to the current state-of-art dense depth estimation methods.