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Found 14 papers, 2 papers with code
PSDR-Room: Single Photo to Scene using Differentiable Rendering
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance.
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.
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout.
TileGen: Tileable, Controllable Material Generation and Capture
The resulting materials are tileable, can be larger than the target image, and are editable by varying the condition.
Differentiable Rendering of Neural SDFs through Reparameterization
Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.
Physically-Based Editing of Indoor Scene Lighting from a Single Image
We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions.
Neural BRDFs: Representation and Operations
Bidirectional reflectance distribution functions (BRDFs) are pervasively used in computer graphics to produce realistic physically-based appearance.
NeuMIP: Multi-Resolution Neural Materials
We also introduce neural offsets, a novel method which allows rendering materials with intricate parallax effects without any tessellation.
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.
MaterialGAN: Reflectance Capture using a Generative SVBRDF Model
We address the problem of reconstructing spatially-varying BRDFs from a small set of image measurements.
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.
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
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.
