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Found 5 papers, 3 papers with code
Towards Full-to-Empty Room Generation with Structure-Aware Feature Encoding and Soft Semantic Region-Adaptive Normalization
In order to acquire photo-realistic and structural consistent background, existing deep learning methods either employ image inpainting approaches or incorporate the learning of the scene layout as an individual task and leverage it later in a not fully differentiable semantic region-adaptive normalization module.
Pano3D: A Holistic Benchmark and a Solid Baseline for $360^o$ Depth Estimation
Pano3D is a new benchmark for depth estimation from spherical panoramas.
Deep Lighting Environment Map Estimation from Spherical Panoramas
We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism.
Restyling Data: Application to Unsupervised Domain Adaptation
Machine learning is driven by data, yet while their availability is constantly increasing, training data require laborious, time consuming and error-prone labelling or ground truth acquisition, which in some cases is very difficult or even impossible.
$360^o$ Surface Regression with a Hyper-Sphere Loss
We present a dataset of $360^o$ images of indoor spaces with their corresponding ground truth surface normal, and train a deep convolutional neural network (CNN) on the task of monocular 360 surface estimation.
