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Found 26 papers, 8 papers with code
LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors
We introduce a solution to large scale Augmented Reality for outdoor scenes by registering camera images to textured Digital Elevation Models (DEMs).
Ranked #2 on
Patch Matching
on HPatches
(using extra training data)
LightIt: Illumination Modeling and Control for Diffusion Models
Our method is the first that enables the generation of images with controllable, consistent lighting and performs on par with specialized relighting state-of-the-art methods.
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.
Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Our key insight is to lift diffusion activations for an object to 3D using a proxy depth, 3D-transform the depth and associated activations, and project them back to image space.
PhotoMat: A Material Generator Learned from Single Flash Photos
Instead, we train a generator for a neural material representation that is rendered with a learned relighting module to create arbitrarily lit RGB images; these are compared against real photos using a discriminator.
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.
PixHt-Lab: Pixel Height Based Light Effect Generation for Image Compositing
To compensate for the lack of geometry in 2D Image compositing, recent deep learning-based approaches introduced a pixel height representation to generate soft shadows and reflections.
Cross-modal Latent Space Alignment for Image to Avatar Translation
We present a novel method for automatic vectorized avatar generation from a single portrait image.
Single View Scene Scale Estimation Using Scale Field
In this paper, we propose a single image scale estimation method based on a novel scale field representation.
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.
Perspective Fields for Single Image Camera Calibration
We propose perspective fields as a representation that models the local perspective properties of an image.
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.
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout.
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.
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering
We achieve this by introducing a 3D-to-2D texture mapping (or surface parameterization) network into volumetric representations.
Neural Reflectance Fields for Appearance Acquisition
We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.
Deep Multi Depth Panoramas for View Synthesis
We propose a learning-based approach for novel view synthesis for multi-camera 360$^{\circ}$ panorama capture rigs.
Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images
We also show that our learned reflectance volumes are editable, allowing for modifying the materials of the captured scenes.
Single View Metrology in the Wild
Most 3D reconstruction methods may only recover scene properties up to a global scale ambiguity.
AutoToon: Automatic Geometric Warping for Face Cartoon Generation
Caricature, a type of exaggerated artistic portrait, amplifies the distinctive, yet nuanced traits of human faces.
Deep Parametric Indoor Lighting Estimation
We present a method to estimate lighting from a single image of an indoor scene.
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.
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.
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 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.
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
