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Found 40 papers, 15 papers with code
Towards a Perceptual Evaluation Framework for Lighting Estimation
Progress in lighting estimation is tracked by computing existing image quality assessment (IQA) metrics on images from standard datasets.
Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning
Conditioned on a target image, such methods extract the target style and combine it with the source image content, keeping coherence between the domains.
EverLight: Indoor-Outdoor Editable HDR Lighting Estimation
In this work, we propose to bridge the gap between these recent trends in the literature, and propose a method which combines a parametric light model with 360{\deg} panoramas, ready to use as HDRI in rendering engines.
Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction
The resulting dataset is a rich representation of indoor scenes which displays a wide range of illuminance and color, and varied types of light sources.
DarSwin: Distortion Aware Radial Swin Transformer
Wide-angle lenses are commonly used in perception tasks requiring a large field of view.
Robust Unsupervised StyleGAN Image Restoration
GAN-based image restoration inverts the generative process to repair images corrupted by known degradations.
Lens Parameter Estimation for Realistic Depth of Field Modeling
Instead, we go further and propose to use a lens-based representation that models the depth of field using two parameters: the blur factor and focus disparity.
The Differentiable Lens: Compound Lens Search over Glass Surfaces and Materials for Object Detection
Recently, joint optimization approaches that design lenses alongside other components of the image acquisition and processing pipeline -- notably, downstream neural networks -- have achieved improved imaging quality or better performance on vision tasks.
Editable Indoor Lighting Estimation
We present a method for estimating lighting from a single perspective image of an indoor scene.
A Deep Perceptual Measure for Lens and Camera Calibration
Image editing and compositing have become ubiquitous in entertainment, from digital art to AR and VR experiences.
Casual Indoor HDR Radiance Capture from Omnidirectional Images
We present PanoHDR-NeRF, a neural representation of the full HDR radiance field of an indoor scene, and a pipeline to capture it casually, without elaborate setups or complex capture protocols.
Robust Scene Inference under Noise-Blur Dual Corruptions
This creates a trade-off between these two kinds of image degradations: motion blur (due to long exposure) vs. noise (due to short exposure), also referred as a dual image corruption pair in this paper.
Overparameterization Improves StyleGAN Inversion
We show that this allows us to obtain near-perfect image reconstruction without the need for encoders nor for altering the latent space after training.
Guided Co-Modulated GAN for 360° Field of View Extrapolation
We propose a method to extrapolate a 360{\deg} field of view from a single image that allows for user-controlled synthesis of the out-painted content.
Matching Feature Sets for Few-Shot Image Classification
In image classification, it is common practice to train deep networks to extract a single feature vector per input image.
ManiFest: Manifold Deformation for Few-shot Image Translation
To enforce feature consistency, our framework learns a style manifold between source and proxy anchor domains (assumed to be composed of large numbers of images).
Image-to-Image Translation with Low Resolution Conditioning
This differs from previous methods that focus on translating a given image style into a target content, our translation approach being able to simultaneously imitate the style and merge the structural information of the LR target.
Ranked #5 on
Image-to-Image Translation
on CelebA-HQ
Mixture-based Feature Space Learning for Few-shot Image Classification
In contrast, we propose to model base classes with mixture models by simultaneously training the feature extractor and learning the mixture model parameters in an online manner.
Deep SVBRDF Estimation on Real Materials
Recent work has demonstrated that deep learning approaches can successfully be used to recover accurate estimates of the spatially-varying BRDF (SVBRDF) of a surface from as little as a single image.
Rain rendering for evaluating and improving robustness to bad weather
In this context, we present a rain rendering pipeline that enables the systematic evaluation of common computer vision algorithms to controlled amounts of rain.
RGB-D-E: Event Camera Calibration for Fast 6-DOF Object Tracking
In this paper, we propose, for the first time, to use an event-based camera to increase the speed of 3D object tracking in 6 degrees of freedom.
Input Dropout for Spatially Aligned Modalities
Computer vision datasets containing multiple modalities such as color, depth, and thermal properties are now commonly accessible and useful for solving a wide array of challenging tasks.
Associative Alignment for Few-shot Image Classification
Few-shot image classification aims at training a model from only a few examples for each of the "novel" classes.
Deep Template-based Object Instance Detection
In this context, we propose a generic 2D object instance detection approach that uses example viewpoints of the target object at test time to retrieve its 2D location in RGB images, without requiring any additional training (i. e. fine-tuning) step.
Deep Parametric Indoor Lighting Estimation
We present a method to estimate lighting from a single image of an indoor scene.
Physics-Based Rendering for Improving Robustness to Rain
Our rendering relies on a physical particle simulator, an estimation of the scene lighting and an accurate rain photometric modeling to augment images with arbitrary amount of realistic rain or fog.
All-Weather Deep Outdoor Lighting Estimation
We use this network to label a large-scale dataset of LDR panoramas with lighting parameters and use them to train our single image outdoor lighting estimation network.
Fast Spatially-Varying Indoor Lighting Estimation
We propose a real-time method to estimate spatiallyvarying indoor lighting from a single RGB image.
Learning Physics-guided Face Relighting under Directional Light
Relighting is an essential step in realistically transferring objects from a captured image into another environment.
Deep Sky Modeling for Single Image Outdoor Lighting Estimation
We propose a data-driven learned sky model, which we use for outdoor lighting estimation from a single image.
Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies
We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i. e., application-specific) metric.
Deep Photovoltaic Nowcasting
Our experiments reveal that the MLP network, already used similarly in previous work, achieves an RMSE skill score of 7% over the commonly-used persistence baseline on the 1-minute future photovoltaic power prediction task.
Learning to Estimate Indoor Lighting from 3D Objects
To achieve this, we developed a deep learning method that is able to encode the latent space of indoor lighting using few parameters and that is trained on a database of environment maps.
Single Day Outdoor Photometric Stereo
Our analysis reveals that partially cloudy days improve the conditioning of the outdoor PS problem while sunny days do not allow the unambiguous recovery of surface normals from photometric cues alone.
A Framework for Evaluating 6-DOF Object Trackers
We present a challenging and realistic novel dataset for evaluating 6-DOF object tracking algorithms.
A Perceptual Measure for Deep Single Image Camera Calibration
This network is trained using automatically generated samples from a large-scale panorama dataset, and considerably outperforms other methods, including recent deep learning-based approaches, in terms of standard L2 error.
Learning to Predict Indoor Illumination from a Single Image
We propose an automatic method to infer high dynamic range illumination from a single, limited field-of-view, low dynamic range photograph of an indoor scene.
Learning High Dynamic Range from Outdoor Panoramas
Outdoor lighting has extremely high dynamic range.
Deep 6-DOF Tracking
We present a temporal 6-DOF tracking method which leverages deep learning to achieve state-of-the-art performance on challenging datasets of real world capture.
Deep Outdoor Illumination Estimation
We present a CNN-based technique to estimate high-dynamic range outdoor illumination from a single low dynamic range image.
Ranked #1 on
Outdoor Light Source Estimation
on SUN360
