We present ObSuRF, a method which turns a single image of a scene into a 3D model
represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to …

We study the problem of inferring an object-centric scene representation from a single
image, aiming to derive a representation that explains the image formation process …

Neural volumetric representations have shown the potential that Multi-layer Perceptrons
(MLPs) can be optimized with multi-view calibrated images to represent scene geometry and …

This paper presents one of the first learning-based NeRF 3D instance segmentation
pipelines, dubbed as Instance Neural Radiance Field, or Instance-NeRF. Taking a NeRF …

We present NeSF, a method for producing 3D semantic fields from posed RGB images
alone. In place of classical 3D representations, our method builds on recent work in implicit …

We present radiance field propagation (RFP), a novel approach to segmenting objects in 3D
during reconstruction given only unlabeled multi-view images of a scene. RFP is derived …

We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object
category models from collections of input images. In contrast to previous work, we are able …

In this paper, we study the problem of 3D scene geometry decomposition and manipulation
from 2D views. By leveraging the recent implicit neural representation techniques …

Towards holistic understanding of 3D scenes a general 3D segmentation method is needed
that can segment diverse objects without restrictions on object quantity or categories while …

We introduce an approach for selecting objects in neural volumetric 3D representations,
such as multi-plane images (MPI) and neural radiance fields (NeRF). Our approach takes a …