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Found 11 papers, 1 papers with code
Learning High-Quality Navigation and Zooming on Omnidirectional Images in Virtual Reality
Our system enables users to effortlessly locate and zoom in on the objects of interest in VR.
Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360 Image Outpainting
To this end, we propose a transformer-based 360 image outpainting framework called Dream360, which can generate diverse, high-fidelity, and high-resolution panoramas from user-selected viewports, considering the spherical properties of 360 images.
Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping
In this paper, we introduce Hi-Map, a novel monocular dense mapping approach based on Neural Radiance Field (NeRF).
SRFNet: Monocular Depth Estimation with Fine-grained Structure via Spatial Reliability-oriented Fusion of Frames and Events
Firstly, we propose an attention-based interactive fusion (AIF) module that applies spatial priors of events and frames as the initial masks and learns the consensus regions to guide the inter-modal feature fusion.
OmniZoomer: Learning to Move and Zoom in on Sphere at High-Resolution
The M\"obius transformation is typically employed to further provide the opportunity for movement and zoom on ODIs, but applying it to the image level often results in blurry effect and aliasing problem.
FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM
We then propose the sliding window sampler to reduce uncertainty by incorporating coherent geometric cues from observed frames during map initialization to enhance convergence.
360$^\circ$ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer
Our key idea is to transfer the scene structural knowledge from the HR image modality and the corresponding LR depth maps to achieve the goal of HR depth estimation without any extra inference cost.
Both Style and Distortion Matter: Dual-Path Unsupervised Domain Adaptation for Panoramic Semantic Segmentation
Moreover, adversarial intra-projection training is proposed to reduce the inherent gap, between the features of the pinhole images and those of the ERP and TP images, respectively.
HRDFuse: Monocular 360°Depth Estimation by Collaboratively Learning Holistic-with-Regional Depth Distributions
Depth estimation from a monocular 360{\deg} image is a burgeoning problem owing to its holistic sensing of a scene.
HRDFuse: Monocular 360deg Depth Estimation by Collaboratively Learning Holistic-With-Regional Depth Distributions
Depth estimation from a monocular 360 image is a burgeoning problem owing to its holistic sensing of a scene.
Deep Learning for Omnidirectional Vision: A Survey and New Perspectives
Omnidirectional image (ODI) data is captured with a 360x180 field-of-view, which is much wider than the pinhole cameras and contains richer spatial information than the conventional planar images.
