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Found 22 papers, 6 papers with code
Generative Multiview Relighting for 3D Reconstruction under Extreme Illumination Variation
We present an approach that reconstructs objects from images taken under different illuminations by first relighting the images under a single reference illumination with a multiview relighting diffusion model and then reconstructing the object's geometry and appearance with a radiance field architecture that is robust to the small remaining inconsistencies among the relit images.
SimVS: Simulating World Inconsistencies for Robust View Synthesis
Novel-view synthesis techniques achieve impressive results for static scenes but struggle when faced with the inconsistencies inherent to casual capture settings: varying illumination, scene motion, and other unintended effects that are difficult to model explicitly.
EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis
We present Exact Volumetric Ellipsoid Rendering (EVER), a method for real-time differentiable emission-only volume rendering.
Flash Cache: Reducing Bias in Radiance Cache Based Inverse Rendering
State-of-the-art techniques for 3D reconstruction are largely based on volumetric scene representations, which require sampling multiple points to compute the color arriving along a ray.
IllumiNeRF: 3D Relighting Without Inverse Rendering
Existing methods for relightable view synthesis -- using a set of images of an object under unknown lighting to recover a 3D representation that can be rendered from novel viewpoints under a target illumination -- are based on inverse rendering, and attempt to disentangle the object geometry, materials, and lighting that explain the input images.
NeRF-Casting: Improved View-Dependent Appearance with Consistent Reflections
Neural Radiance Fields (NeRFs) typically struggle to reconstruct and render highly specular objects, whose appearance varies quickly with changes in viewpoint.
Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis
Third, we minimize the binary entropy of the opacity values, which facilitates the extraction of surface geometry by encouraging opacity values to binarize towards the end of training.
Boundary Attention: Learning curves, corners, junctions and grouping
We present a lightweight network that infers grouping and boundaries, including curves, corners and junctions.
Nuvo: Neural UV Mapping for Unruly 3D Representations
We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques.
ReconFusion: 3D Reconstruction with Diffusion Priors
3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes.
Generative Powers of Ten
We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene, e. g., ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches.
Eclipse: Disambiguating Illumination and Materials using Unintended Shadows
We present a method based on differentiable Monte Carlo ray tracing that uses images of an object to jointly recover its spatially-varying materials, the surrounding illumination environment, and the shapes of the unseen light occluders who inadvertently cast shadows upon it.
Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields
Neural Radiance Field training can be accelerated through the use of grid-based representations in NeRF's learned mapping from spatial coordinates to colors and volumetric density.
Neural Microfacet Fields for Inverse Rendering
We present Neural Microfacet Fields, a method for recovering materials, geometry, and environment illumination from images of a scene.
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis.
MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
We design a lossless procedure for baking the parameterization used during training into a model that achieves real-time rendering while still preserving the photorealistic view synthesis quality of a volumetric radiance field.
Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields
Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a scene as a continuous volumetric function, parameterized by multilayer perceptrons that provide the volume density and view-dependent emitted radiance at each location.
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
Though neural radiance fields (NeRF) have demonstrated impressive view synthesis results on objects and small bounded regions of space, they struggle on "unbounded" scenes, where the camera may point in any direction and content may exist at any distance.
Ranked #8 on
Novel View Synthesis
on Tanks and Temples
Field of Junctions: Extracting Boundary Structure at Low SNR
We introduce a bottom-up model for simultaneously finding many boundary elements in an image, including contours, corners and junctions.
Toward a Universal Model for Shape From Texture
An equilibrium of this game yields two things: an estimate of the 2. 5D surface from the shape process, and a stochastic texture synthesis model from the texture process.
Unique Geometry and Texture from Corresponding Image Patches
We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process.
Crossing the Road Without Traffic Lights: An Android-based Safety Device
The indication can be by sound, display, vibration, and various communication modalities provided by the Android device.
