# Real-time Multi-view Omnidirectional Depth Estimation for Real Scenarios based on Teacher-Student Learning with Unlabeled Data

arXiv: https://arxiv.org/abs/2409.07843 · HTML: https://mlsanity.com/paper/2409.07843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 39 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 23 of 23)

- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [AANet: Adaptive Aggregation Network for Efficient Stereo Matching](https://mlsanity.com/paper/2004.09548/) (2020)
- [Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation](https://mlsanity.com/paper/2203.11483/) (2022)
- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches](https://mlsanity.com/paper/1510.05970/) (2015)
- [Attention Concatenation Volume for Accurate and Efficient Stereo Matching](https://mlsanity.com/paper/2203.02146/) (2022)
- [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)
- [Spherical View Synthesis for Self-Supervised 360 Depth Estimation](https://mlsanity.com/paper/1909.08112/) (2019)
- [Self-Supervised Learning of Depth and Camera Motion from 360deg Videos](https://mlsanity.com/paper/1811.05304/) (2018)
- [360SD-Net: 360deg Stereo Depth Estimation with Learnable Cost Volume](https://mlsanity.com/paper/1911.04460/) (2019)
- [EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection](https://mlsanity.com/paper/1903.01700/) (2019)
- [BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation](https://mlsanity.com/paper/2209.02952/) (2022)
- [SweepNet: Wide-baseline Omnidirectional Depth Estimation](https://mlsanity.com/paper/1902.10904/) (2019)
- [OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching](https://mlsanity.com/paper/1908.06257/) (2019)
- [360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network](https://mlsanity.com/paper/2202.08010/) (2022)
- [360^circ Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron](https://mlsanity.com/paper/2007.06891/) (2020)
- [Unsupervised OmniMVS: Efficient Omnidirectional Depth Inference via Establishing Pseudo-Stereo Supervision](https://mlsanity.com/paper/2302.09922/) (2023)
- [RomniStereo: Recurrent Omnidirectional Stereo Matching](https://mlsanity.com/paper/2401.04345/) (2024)

## Abstract (arXiv)

Omnidirectional depth estimation enables efficient 3D perception over a full 360-degree range. However, in real-world applications such as autonomous driving and robotics, achieving real-time performance and robust cross-scene generalization remains a significant challenge for existing algorithms. In this paper, we propose a real-time omnidirectional depth estimation method for edge computing platforms named Rt-OmniMVS, which introduces the Combined Spherical Sweeping method and implements the lightweight network structure to achieve real-time performance on edge computing platforms. To achieve high accuracy, robustness, and generalization in real-world environments, we introduce a teacher-student learning strategy. We leverage the high-precision stereo matching method as the teacher model to predict pseudo labels for unlabeled real-world data, and utilize data and model augmentation techniques for training to enhance performance of the student model Rt-OmniMVS. We also propose HexaMODE, an omnidirectional depth sensing system based on multi-view fisheye cameras and edge computation device. A large-scale hybrid dataset contains both unlabeled real-world data and synthetic data is collected for model training. Experiments on public datasets demonstrate that proposed method achieves results comparable to state-of-the-art approaches while consuming significantly less resource. The proposed system and algorithm also demonstrate high accuracy in various complex real-world scenarios, both indoors and outdoors, achieving an inference speed of 15 frames per second on edge computing platforms.