# Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360deg Panoramic Imagery

arXiv: https://arxiv.org/abs/1808.06253 · HTML: https://mlsanity.com/paper/1808.06253/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2018.

## Built on by (most cited first, 30 of 35)

- [Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation](https://mlsanity.com/paper/2203.01452/) (2022)
- [360MonoDepth: High-Resolution 360deg Monocular Depth Estimation](https://mlsanity.com/paper/2111.15669/) (2021)
- [Capturing Omni-Range Context for Omnidirectional Segmentation](https://mlsanity.com/paper/2103.05687/) (2021)
- [Behind Every Domain There is a Shift: Adapting Distortion-aware Vision Transformers for Panoramic Semantic Segmentation](https://mlsanity.com/paper/2207.11860/) (2022)
- [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)
- [SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation](https://mlsanity.com/paper/2306.03403/) (2023)
- [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)
- [Single Frame Semantic Segmentation Using Multi-Modal Spherical Images](https://mlsanity.com/paper/2308.09369/) (2023)
- [SphereDiffusion: Spherical Geometry-Aware Distortion Resilient Diffusion Model](https://mlsanity.com/paper/2403.10044/) (2024)
- [Cylin-Painting: Seamless 360textdegree Panoramic Image Outpainting and Beyond](https://mlsanity.com/paper/2204.08563/) (2022)
- [Complementary Bi-directional Feature Compression for Indoor 360deg Semantic Segmentation with Self-distillation](https://mlsanity.com/paper/2207.02437/) (2022)
- [3D Scene Geometry Estimation from 360^circ Imagery: A Survey](https://mlsanity.com/paper/2401.09252/) (2024)
- [Enabling Visual Recognition at Radio Frequency](https://mlsanity.com/paper/2405.19516/) (2024)
- [PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation](https://mlsanity.com/paper/2202.01323/) (2022)
- [RomniStereo: Recurrent Omnidirectional Stereo Matching](https://mlsanity.com/paper/2401.04345/) (2024)
- [PanoSplatt3R: Leveraging Perspective Pretraining for Generalized Unposed Wide-Baseline Panorama Reconstruction](https://mlsanity.com/paper/2507.21960/) (2025)
- [S2Net: Accurate Panorama Depth Estimation on Spherical Surface](https://mlsanity.com/paper/2301.05845/) (2023)
- [Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras](https://mlsanity.com/paper/2107.08862/) (2021)
- [Outdoor Monocular Depth Estimation: A Research Review](https://mlsanity.com/paper/2205.01399/) (2022)
- [Pantheon360: Taming Digital Twin Generation via 3D-Aware 360deg Video Diffusion](https://mlsanity.com/paper/2605.25449/) (2026)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor Scenes](https://mlsanity.com/paper/2108.08076/) (2021)
- [HDPV-SLAM: Hybrid Depth-augmented Panoramic Visual SLAM for Mobile Mapping System with Tilted LiDAR and Panoramic Visual Camera](https://mlsanity.com/paper/2301.11823/) (2023)
- [MaGRITTe: Manipulative and Generative 3D Realization from Image, Topview and Text](https://mlsanity.com/paper/2404.00345/) (2024)
- [SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion](https://mlsanity.com/paper/2502.05859/) (2025)
- [Depth Anything in 360^circ: Towards Scale Invariance in the Wild](https://mlsanity.com/paper/2512.22819/) (2025)
- [Does it work outside this benchmark? Introducing the Rigid Depth Constructor tool, depth validation dataset construction in rigid scenes for the masses](https://mlsanity.com/paper/2103.15970/) (2021)
- [Panoramic Image-to-Image Translation](https://mlsanity.com/paper/2304.04960/) (2023)
- [360 in the Wild: Dataset for Depth Prediction and View Synthesis](https://mlsanity.com/paper/2406.18898/) (2024)

## Abstract (arXiv)

Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360deg panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360deg panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt a contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery.