mlsanityData to 7 Oct 2026

Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap

arXiv 2203.09733 ↗First posted 18 Mar 2022cs.CV eess.IVcited by 7

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Abstract (arXiv)

Estimating the depth of omnidirectional images is more challenging than that of normal field-of-view (NFoV) images because the varying distortion can significantly twist an object's shape. The existing methods suffer from troublesome distortion while estimating the depth of omnidirectional images, leading to inferior performance. To reduce the negative impact of the distortion influence, we propose a distortion-tolerant omnidirectional depth estimation algorithm using a dual-cubemap. It comprises two modules: Dual-Cubemap Depth Estimation (DCDE) module and Boundary Revision (BR) module. In DCDE module, we present a rotation-based dual-cubemap model to estimate the accurate NFoV depth, reducing the distortion at the cost of boundary discontinuity on omnidirectional depths. Then a boundary revision module is designed to smooth the discontinuous boundaries, which contributes to the precise and visually continuous omnidirectional depths. Extensive experiments demonstrate the superiority of our method over other state-of-the-art solutions.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 21 references that are in the map. First posted on arXiv 18 Mar 2022.

Builds on

15 of 21 references.

References · 15

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
  3. 2017Matterport3D: Learning from RGB-D Data in Indoor Environmentscited by 1,354
  4. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,264
  5. 2018Deep Ordinal Regression Network for Monocular Depth Estimationcited by 721
  6. 2018Spherical CNNscited by 452
  7. 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 404
  8. 2016Deeper Depth Prediction with Fully Convolutional Residual Networkscited by 402
  9. 2017Learning SO(3) Equivariant Representations with Spherical CNNscited by 244
  10. 2018OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramascited by 98
  11. 2018Cube Padding for Weakly-Supervised Saliency Prediction in 360deg Videoscited by 66
  12. 2018SuperDepth: Self-Supervised, Super-Resolved Monocular Depth Estimationcited by 64
3 more
  1. 2018Kernel Transformer Networks for Compact Spherical Convolutioncited by 56
  2. 2017Learning Spherical Convolution for Fast Features from 360deg Imagerycited by 22
  3. 2016Active Regression with Adaptive Huber Losscited by 3

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Cited by year

Citing papers per year: 2022: 3; 2023: 2; 2024: 1; 2025: 1.

7 papers.

  1. 2023Disentangling Orthogonal Planes for Indoor Panoramic Room Layout Estimation with Cross-Scale Distortion Awarenesscited by 14
  2. 2022Complementary Bi-directional Feature Compression for Indoor 360deg Semantic Segmentation with Self-distillationcited by 12
  3. 2022High-Resolution Depth Estimation for 360-degree Panoramas through Perspective and Panoramic Depth Images Registrationcited by 10
  4. 2023360 Layout Estimation via Orthogonal Planes Disentanglement and Multi-view Geometric Consistency Perceptioncited by 5
  5. 2024Revisiting 360 Depth Estimation with PanoGabor: A New Fusion Perspectivecited by 5
  6. 2022Neural Contourlet Network for Monocular 360 Depth Estimationcited by 3
  7. 2025PAGCNet: A Pose-Aware and Geometry Constrained Framework for Panoramic Depth Estimationcited by 0