♦ Deep Learning for Omnidirectional Vision: A Survey and New Perspectives

Referenced paper : Deep Learning for Omnidirectional Vision: A Survey and New Perspectives

Table of Content

        Omnidirectional image (ODI) data is captured with a 360°×180° field-of-view and omnidirectional vision has attracted booming attention due to its more advantageous performance in numerous applications. Our survey presents a systematic and comprehensive review and analysis of the recent progress in Deep Learning methods for omnidirectional vision.

        Especially, we create this open-source repository to provide a taxonomy of all the mentioned works and code links in the survey. We will keep updating our open-source repository with new works in this area and hope it can shed light on future research and build a community for researchers on omnidirectional vision.

  • Efficient 3D Room Shape Recovery (traditional)
  • MVLayoutNet: 3D layout reconstruction with multi-view panoramas
  • HeadFusion: 360◦Head Pose tracking combining3D Morphable Model and 3D Reconstruction
  • Manhattan Room Layout Reconstruction from a Single 360◦ image: A Comparative Study of State-of-the-art Methods
  • Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Network
  • Robust 3D reconstruction with omni-directional camera based on structure from motion
  • CAN WE USE LOW-COST 360 DEGREE CAMERAS TO CREATE ACCURATE 3D MODELS
  • Learning Spherical Convolution for Fast Features from 360°Imagery
  • Learning SO(3) Equivariant Representationswith Spherical CNNs (点云数据集)
  • SpherePHD: Applying CNNs on a Spherical PolyHeDron Representation of 360-degree Images
  • CONCENTRIC SPHERICAL GNN FOR 3D REPRESENTATION LEARNING
  • SPHERICAL CNNS
  • Rotation Equivariant Graph Convolutional Network for Spherical Image Classification
  • Self-supervised Representation Learning Using 360◦ Data
  • SphereNet: Learning Spherical Representations for Detection and Classification in Omnidirectional Images
  • BIPS: Bi-modal Indoor Panorama Synthesis via Residual Depth-aided Adversarial Learning
  • Sat2Vid: Street-view Panoramic Video Synthesis from a Single Satellite Image
  • Snap Angle Prediction for 360◦ Panoramas
  • Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
  • Refer360-degree: A Referring Expression Recognition Dataset in 360-degree Images
  • 360-Indoor: Towards Learning Real-World Objects in 360◦ Indoor Equirectangular Images
  • Recognizing Scene Viewpoint using Panoramic Place Representation
  • Zillow Indoor Dataset: Annotated Floor Plans With 360o Panoramas and 3D Room Layouts
  • Matterport3D: Learning from RGB-D Data in Indoor Environments
  • A Saliency Dataset for 360-Degree Videos
  • 360-degree Video Gaze Behaviour: A Ground-Truth Data Set and a Classification Algorithm for Eye Movements
  • AVTrack360: An open Dataset and Soware recording people’sHead Rotations watching 360◦Videos on an HMD
  • A Large-scale Compressed 360-Degree Spherical Image database: from Subjective Quality Evaluation to Objective Model Comparison
  • A Dataset of Head and Eye Movements for 360 Degree Images
  • 360 Depth Estimation in the Wild -- The Depth360 Dataset and the SegFuse Network
  • 360MonoDepth: High-Resolution 360° Monocular Depth Estimation
  • ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation
  • BiFuse: Monocular 360-degree Depth Estimation via Bi-Projection Fusion
  • Improving 360◦ Monocular Depth Estimation via Non-local Dense Prediction Transformer and Joint Supervised and Self-supervised Learning
  • Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images
  • GLPanoDepth: Global-to-Local Panoramic Depth Estimation
  • Geometric Structure Based and Regularized Depth Estimation From 360-degree Indoor Imagery
  • OmniFusion : 360 Monocular Depth Estimation via Geometry-Aware Fusion
  • PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation
  • SliceNet: deep dense depth estimation from a single indoor panorama using a slice-based representation
  • OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas
  • Spherical View Synthesis for Self-Supervised 360-degree Depth Estimation
  • Looking here or there? Gaze Following in 360-Degree Images
  • Gaze Prediction in Dynamic 360◦ Immersive Videos
  • Self-view Grounding Given a Narrated 360◦ Video
  • Gaze360: Physically Unconstrained Gaze Estimation in the Wild
  • See360: Novel Panoramic View Interpolation
  • A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360-degree Video
  • Pseudocylindrical Convolutions for LearnedOmnidirectional Image Compression
  • LAU-Net: Latitude Adaptive Upscaling Network for Omnidirectional Image Super-resolution
  • SphereSR: 360-degree Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation
  • Panoramic Image Reflection Removal
  • Privacy Protection in Street-View Panoramas using Depth and Multi-View Imagery
  • Field-of-View IoU for Object Detection in 360{%20deg} Images
  • Deep 360 Pilot: Learning a Deep Agent for Piloting through 360◦ Sports Videos
  • Spherical Criteria for Fast and Accurate 360-degree Object Detection
  • Kernel Transformer Networks for Compact Spherical Convolution
  • PICCOLO: Point Cloud-Centric Omnidirectional Localization (traditional)
  • OmniSLAM: Omnidirectional Localization and Dense Mapping for Wide-baseline Multi-camera Systems
  • Rotation Equivariant Orientation Estimation for Omnidirectional Localization
  • Learning High Dynamic Range from Outdoor Panoramas
  • Spatially-Varying Outdoor Lighting Estimation from Intrinsics
  • Minimal Solutions for Panoramic Stitching Given Gravity Prior (traditional)
  • AtlantaNet: Inferring the 3D Indoor Layout from a Single 360◦ Image beyond the Manhattan World Assumption
  • Deep3DLayout: 3D Reconstruction of an Indoor Layout from a Spherical Panoramic Image
  • Pano2CAD: Room Layout From A Single Panorama Image
  • DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama
  • HorizonNet: Learning Room Layout with 1D Representation and Pano Stretch Data Augmentation
  • OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas
  • SSLayout360: Semi-Supervised Indoor Layout Estimation from 360◦ Panorama
  • Joint 3D Layout and Depth Prediction from a Single Indoor Panorama Image
  • Corners for Layout: End-to-End Layout Recovery From 360 Images
  • LED2-Net: Monocular 360◦ Layout Estimation via Differentiable Depth Rendering
  • Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
  • Cube Padding for Weakly-Supervised Saliency Prediction in 360◦Videos
  • SalGCN: Saliency Prediction for 360-Degree Images Based on Spherical Graph Convolutional Networks
  • Rethinking 360° Image Visual Attention Modelling with Unsupervised Learning
  • Saliency Detection in 360◦Videos
  • Your Attention is Unique: Detecting 360-Degree Video Saliency in Head-Mounted Display for Head Movement Prediction
  • 360-aware saliency estimation with conventional image saliency predictors **(traditional)
  • DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization
  • PanoContext: A Whole-Room 3D Context Model for Panoramic Scene Understanding (traditional)
  • Lighting, Reflectance and Geometry Estimation from 360◦ Panoramic Stereo
  • HoHoNet: 360 Indoor Holistic Understanding with Latent Horizontal Features
  • Automatic 3D Indoor Scene Modeling from Single Panorama
  • Im2Pano3D: Extrapolating 360° Structure and Semantics Beyond the Field of View
  • Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360◦ Panoramic Imagery
  • Recovering 3D existing-conditions of indoor structures from spherical images (traditional)
  • OmniMVS: End-to-End Learning for Omnidirectional Stereo Matching
  • Extreme Structure from Motion for Indoor Panoramas without Visual Overlaps
  • State-of-the-Art in 360° Video/Image Processing: Perception, Assessment and Compression
  • A Survey on Adaptive 360◦ Video Streaming: Solutions, Challenges and Opportunities
  • Annotated 360-Degree Image and Video Databases: A Comprehensive Survey
  • 3D Scene Geometry Estimation from 360◦ Imagery: A Survey
  • 360o Camera Alignment via Segmentation
  • Deep Upright Adjustment of 360 Panoramas Using Multiple Roll Estimations
  • VRSA Net: VR Sickness Assessment Considering Exceptional Motion for 360° VR Video
  • Advanced Spherical Motion Model and Local Padding for 360° Video Compression (traditional)
  • Learning Compressible 360◦Video Isomers
  • A Memory Network Approach for Story-Based Temporal Summarization of 360° Videos
  • Pano2Vid: Automatic Cinematography for Watching 360-degree Videos
  • Automatic Content-aware Projection for 360◦ Videos (traditional)
  • Deep Multi Depth Panoramas for View Synthesis
  • Viewport Proposal CNN for 360° Video Quality Assessment
  • Cross-Reference Stitching Quality Assessmentfor 360◦Omnidirectional Images
  • Cubemap-Based Perception-Driven Blind Quality Assessment for 360-degree Images
  • MC360IQA: A Multi-channel CNN for Blind360-Degree Image Quality Assessment
  • Visual Question Answering on 360{%20deg} Images
  • Pano-AVQA: Grounded Audio-Visual Question Answering on 360-degree Videos
  • Transfer beyond the Field of View: Dense Panoramic Semantic Segmentation via Unsupervised Domain Adaptation
  • What’s in my Room? Object Recognition on Indoor Panoramic Images
  • Bending Reality: Distortion-aware Transformers for Adapting to Panoramic Semantic Segmentation
  • Omnisupervised Omnidirectional Semantic Segmentation
  • DensePASS: Dense Panoramic Semantic Segmentation via UnsupervisedDomain Adaptation with Attention-Augmented Context Exchange
  • DS-PASS: Detail-Sensitive Panoramic Annular Semantic Segmentation
  • Capturing Omni-Range Context for Omnidirectional Segmentation
  • Orientation-Aware Semantic Segmentation on Icosahedron Spheres

Citation

If you found our survey helpful for your research, please cite our paper as:

@article{Ai2022DeepLF,
  title={Deep Learning for Omnidirectional Vision: A Survey and New Perspectives},
  author={Hao Ai and Zidong Cao and Jin Zhu and Haotian Bai and Yucheng Chen and Ling Wang},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.10468}
}