# Distortion-aware Monocular Depth Estimation for Omnidirectional Images

arXiv: https://arxiv.org/abs/2010.08942 · HTML: https://mlsanity.com/paper/2010.08942/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation](https://mlsanity.com/paper/2203.09283/) (2022)
- [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)
- [Full Surround Monodepth from Multiple Cameras](https://mlsanity.com/paper/2104.00152/) (2021)
- [ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation](https://mlsanity.com/paper/2112.14440/) (2021)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)
- [C3Fusion: Consistent Contrastive Colon Fusion, Towards Deep SLAM in Colonoscopy](https://mlsanity.com/paper/2206.01961/) (2022)
- [Estimating Depth of Monocular Panoramic Image with Teacher-Student Model Fusing Equirectangular and Spherical Representations](https://mlsanity.com/paper/2405.16858/) (2024)
- [Revisiting 360 Depth Estimation with PanoGabor: A New Fusion Perspective](https://mlsanity.com/paper/2408.16227/) (2024)
- [Hybrid Skip: A Biologically Inspired Skip Connection for the UNet Architecture](https://mlsanity.com/paper/2207.04721/) (2022)
- [Neural Contourlet Network for Monocular 360 Depth Estimation](https://mlsanity.com/paper/2208.01817/) (2022)
- [SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion](https://mlsanity.com/paper/2502.05859/) (2025)
- [Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding](https://mlsanity.com/paper/2212.05040/) (2022)
- [360 in the Wild: Dataset for Depth Prediction and View Synthesis](https://mlsanity.com/paper/2406.18898/) (2024)
- [Neural Camera Models](https://mlsanity.com/paper/2208.12903/) (2022)
- [Cross360: 360deg Monocular Depth Estimation via Cross Projections Across Scales](https://mlsanity.com/paper/2601.17271/) (2026)
- [Decoupling Spherical Reasoning from Dense Prediction for 360 Depth Estimation](https://mlsanity.com/paper/2609.38856/) (2026)

## Abstract (arXiv)

A main challenge for tasks on panorama lies in the distortion of objects among images. In this work, we propose a Distortion-Aware Monocular Omnidirectional (DAMO) dense depth estimation network to address this challenge on indoor panoramas with two steps. First, we introduce a distortion-aware module to extract calibrated semantic features from omnidirectional images. Specifically, we exploit deformable convolution to adjust its sampling grids to geometric variations of distorted objects on panoramas and then utilize a strip pooling module to sample against horizontal distortion introduced by inverse gnomonic projection. Second, we further introduce a plug-and-play spherical-aware weight matrix for our objective function to handle the uneven distribution of areas projected from a sphere. Experiments on the 360D dataset show that the proposed method can effectively extract semantic features from distorted panoramas and alleviate the supervision bias caused by distortion. It achieves state-of-the-art performance on the 360D dataset with high efficiency.