Spherical CNNs generalize CNNs to functions on the sphere, by using sphe...
A critical obstacle preventing NeRF models from being deployed broadly i...
We present a method for joint alignment of sparse in-the-wild image
coll...
We present a system for accurately predicting stable orientations for di...
Neural rendering has received tremendous attention since the advent of N...
Classical light field rendering for novel view synthesis can accurately
...
Single image pose estimation is a fundamental problem in many vision and...
State-of-the-art deep learning systems often require large amounts of da...
Symmetric orthogonalization via SVD, and closely related procedures, are...
Learning equivariant representations is a promising way to reduce sample...
Group equivariant neural networks have been explored in the past few yea...
Several approaches to 3D vision tasks process multiple views of the inpu...
Spherical convolutional networks have been introduced recently as tools ...
With the recent proliferation of consumer-grade 360 cameras, it is
worth...
3D object classification and retrieval presents many challenges that are...
Convolutional neural networks (CNNs) are inherently equivariant to
trans...