Humans effortlessly infer the 3D shape of objects. What computations und...
Denoising diffusion models are a powerful type of generative models used...
Reconstruction of 3D neural fields from posed images has emerged as a
pr...
We introduce a method for novel view synthesis given only a single
wide-...
Differentiable volumetric rendering is a powerful paradigm for 3D
recons...
Human perception reliably identifies movable and immovable parts of 3D
s...
Emerging neural radiance fields (NeRF) are a promising scene representat...
Neural scene representations, both continuous and discrete, have recentl...
We present Neural Descriptor Fields (NDFs), an object representation tha...
Recent advances in machine learning have created increasing interest in
...
Deep neural networks have been used widely to learn the latent structure...
Humans have a strong intuitive understanding of the 3D environment aroun...
Implicit representations of geometry, such as occupancy fields or signed...
Inferring representations of 3D scenes from 2D observations is a fundame...
Neural implicit shape representations are an emerging paradigm that offe...
Implicitly defined, continuous, differentiable signal representations
pa...
Efficient rendering of photo-realistic virtual worlds is a long standing...
Biological vision infers multi-modal 3D representations that support
rea...
The advent of deep learning has given rise to neural scene representatio...
In this work, we address the lack of 3D understanding of generative neur...
Traditional cinematography has relied for over a century on a
well-estab...
A broad class of problems at the core of computational imaging, sensing,...
Real-world sensors suffer from noise, blur, and other imperfections that...
Understanding how people explore immersive virtual environments is cruci...