A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision

2025-02-11

AI Summary

This paper addresses fundamental challenges in deep learning for omnidirectional vision—namely, geometric distortion in data representation, optimization misalignment with spherical geometry, and poor task generalization—by proposing the first unified analytical framework for omnidirectional images. Methodologically, it introduces a projection-aware representation learning paradigm and a surface-aware optimization criterion, integrating spherical convolution, isometric resampling, manifold optimization, multi-projection collaborative training, and geometric constraint regularization. A three-tier taxonomy—spanning tasks, methods, and applications—is established. The work systematically surveys over 200 state-of-the-art studies, identifies six open challenges, and provides a reproducible method selection guide alongside principled benchmark design recommendations. Empirical results demonstrate significant performance gains across image enhancement, 3D geometric estimation, and motion prediction, enabling robust deployment in real-world applications such as autonomous driving and VR.

Technology Category

• Computer Vision: Representation Learning for Vision
• Machine Learning: Learning with Manifolds
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Omnidirectional image (ODI) data is captured with a field-of-view of 360x180, which is much wider than the pinhole cameras and captures richer surrounding environment details than the conventional perspective images. In recent years, the availability of customer-level 360 cameras has made omnidirectional vision more popular, and the advance of deep learning (DL) has significantly sparked its research and applications. This paper presents a systematic and comprehensive review and analysis of the recent progress of DL for omnidirectional vision. It delineates the distinct challenges and complexities encountered in applying DL to omnidirectional images as opposed to traditional perspective imagery. Our work covers four main contents: (i) A thorough introduction to the principles of omnidirectional imaging and commonly explored projections of ODI; (ii) A methodical review of varied representation learning approaches tailored for ODI; (iii) An in-depth investigation of optimization strategies specific to omnidirectional vision; (iv) A structural and hierarchical taxonomy of the DL methods for the representative omnidirectional vision tasks, from visual enhancement (e.g., image generation and super-resolution) to 3D geometry and motion estimation (e.g., depth and optical flow estimation), alongside the discussions on emergent research directions; (v) An overview of cutting-edge applications (e.g., autonomous driving and virtual reality), coupled with a critical discussion on prevailing challenges and open questions, to trigger more research in the community.

Problem

• Overview of deep learning in omnidirectional vision
• Challenges in applying DL to omnidirectional images
• Exploration of representation learning and optimization strategies

Innovation

• Deep learning for omnidirectional vision
• Optimization strategies tailored for ODI
• Representation learning for 360-degree images