In this work, we investigate the problem of creating high-fidelity 3D content from only a single
image. This is inherently challenging: it essentially involves estimating the underlying 3D …

We address the problem of synthesizing novel views from a monocular video depicting a
complex dynamic scene. State-of-the-art methods based on temporally varying Neural …

We present a super-fast convergence approach to reconstructing the per-scene radiance
field from a set of images that capture the scene with known poses. This task, which is often …

Neural Radiance Fields (NeRF) is a popular view synthesis technique that represents a
scene as a continuous volumetric function, parameterized by multilayer perceptrons that …

This paper presents a novel grid-based NeRF called F^ 2-NeRF (Fast-Free-NeRF) for novel
view synthesis, which enables arbitrary input camera trajectories and only costs a few …

We introduce pixelSplat a feed-forward model that learns to reconstruct 3D radiance fields
parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time …

Neural radiance fields (NeRF) encode a scene into a neural representation that enables
photo-realistic rendering of novel views. However, a successful reconstruction from RGB …

Dynamic radiance field reconstruction methods aim to model the time-varying structure and
appearance of a dynamic scene. Existing methods, however, assume that accurate camera …

The goal of this work is to perform 3D reconstruction and novel view synthesis from data
captured by scanning platforms commonly deployed for world mapping in urban outdoor …

The rendering procedure used by neural radiance fields (NeRF) samples a scene with a
single ray per pixel and may therefore produce renderings that are excessively blurred or …