# Spherical View Synthesis for Self-Supervised 360 Depth Estimation

arXiv: https://arxiv.org/abs/1909.08112 · HTML: https://mlsanity.com/paper/1909.08112/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 30 of 51)

- [UniFuse: Unidirectional Fusion for 360circ Panorama Depth Estimation](https://mlsanity.com/paper/2102.03550/) (2021)
- [OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion](https://mlsanity.com/paper/2203.00838/) (2022)
- [360MonoDepth: High-Resolution 360deg Monocular Depth Estimation](https://mlsanity.com/paper/2111.15669/) (2021)
- [Review on Panoramic Imaging and Its Applications in Scene Understanding](https://mlsanity.com/paper/2205.05570/) (2022)
- [EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation](https://mlsanity.com/paper/2304.07803/) (2023)
- [ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation](https://mlsanity.com/paper/2112.14440/) (2021)
- [Deep Learning for Omnidirectional Vision: A Survey and New Perspectives](https://mlsanity.com/paper/2205.10468/) (2022)
- [Depth Anywhere: Enhancing 360 Monocular Depth Estimation via Perspective Distillation and Unlabeled Data Augmentation](https://mlsanity.com/paper/2406.12849/) (2024)
- [Pano3D: A Holistic Benchmark and a Solid Baseline for 360o Depth Estimation](https://mlsanity.com/paper/2109.02749/) (2021)
- [BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation](https://mlsanity.com/paper/2209.02952/) (2022)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion](https://mlsanity.com/paper/2203.09855/) (2022)
- [360Roam: Real-Time Indoor Roaming Using Geometry-Aware 360^circ Radiance Fields](https://mlsanity.com/paper/2208.02705/) (2022)
- [Improving 360 Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning](https://mlsanity.com/paper/2109.10563/) (2021)
- [360BEV: Panoramic Semantic Mapping for Indoor Bird's-Eye View](https://mlsanity.com/paper/2303.11910/) (2023)
- [Layout-Guided Novel View Synthesis from a Single Indoor Panorama](https://mlsanity.com/paper/2103.17022/) (2021)
- [GLPanoDepth: Global-to-Local Panoramic Depth Estimation](https://mlsanity.com/paper/2202.02796/) (2022)
- [360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network](https://mlsanity.com/paper/2202.08010/) (2022)
- [PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes](https://mlsanity.com/paper/2106.00446/) (2021)
- [High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registration](https://mlsanity.com/paper/2210.10414/) (2022)
- [RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion](https://mlsanity.com/paper/2309.00655/) (2023)
- [3D Scene Geometry Estimation from 360^circ Imagery: A Survey](https://mlsanity.com/paper/2401.09252/) (2024)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)
- [Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision](https://mlsanity.com/paper/2302.09922/) (2023)
- [Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas](https://mlsanity.com/paper/2102.03939/) (2021)
- [360deg Optical Flow using Tangent Images](https://mlsanity.com/paper/2112.14331/) (2021)
- [Casual 6-DoF: free-viewpoint panorama using a handheld 360 camera](https://mlsanity.com/paper/2203.16756/) (2022)
- [HiMODE: A Hybrid Monocular Omnidirectional Depth Estimation Model](https://mlsanity.com/paper/2204.05007/) (2022)
- [OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas](https://mlsanity.com/paper/2202.08752/) (2022)
- [Convolution kernel adaptation to calibrated fisheye](https://mlsanity.com/paper/2402.01456/) (2024)

## Abstract (arXiv)

Learning based approaches for depth perception are limited by the availability of clean training data. This has led to the utilization of view synthesis as an indirect objective for learning depth estimation using efficient data acquisition procedures. Nonetheless, most research focuses on pinhole based monocular vision, with scarce works presenting results for omnidirectional input. In this work, we explore spherical view synthesis for learning monocular 360 depth in a self-supervised manner and demonstrate its feasibility. Under a purely geometrically derived formulation we present results for horizontal and vertical baselines, as well as for the trinocular case. Further, we show how to better exploit the expressiveness of traditional CNNs when applied to the equirectangular domain in an efficient manner. Finally, given the availability of ground truth depth data, our work is uniquely positioned to compare view synthesis against direct supervision in a consistent and fair manner. The results indicate that alternative research directions might be better suited to enable higher quality depth perception. Our data, models and code are publicly available at https://vcl3d.github.io/SphericalViewSynthesis/.