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Found 17 papers, 10 papers with code
Deep Blending for Free-Viewpoint Image-Based-Rendering
We present a new deep learning approach to blending for IBR, in which we use held-out real image data to learn blending weights to combine input photo contributions.
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.
Baking Neural Radiance Fields for Real-Time View Synthesis
Neural volumetric representations such as Neural Radiance Fields (NeRF) have emerged as a compelling technique for learning to represent 3D scenes from images with the goal of rendering photorealistic images of the scene from unobserved viewpoints.
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields
A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space.
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.
NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images
By rendering raw output images from the resulting NeRF, we can perform novel high dynamic range (HDR) view synthesis tasks.
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.
MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures
Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views.
Ranked #1 on
Novel View Synthesis
on Mip-NeRF 360
AligNeRF: High-Fidelity Neural Radiance Fields via Alignment-Aware Training
Neural Radiance Fields (NeRFs) are a powerful representation for modeling a 3D scene as a continuous function.
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.
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.
Vox-E: Text-guided Voxel Editing of 3D Objects
Our method takes oriented 2D images of a 3D object as input and learns a grid-based volumetric representation of 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.
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.
Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion
We compare our approach to 3D inpainting methods that focus on object removal.
SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration
Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive frame rates.
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.
