232 citations · 334 across the 7 of their papers we have counts for
13 papers
CodeNeRF: Disentangled Neural Radiance Fields for Object Categories
Wonbong Jang, Lourdes Agapito
CodeNeRF is an implicit 3D neural representation that learns the variation of object shapes and textures across a category and can be trained, from a set of posed images, to synthe…
DSP-SLAM: Object Oriented SLAM with Deep Shape Priors
Jingwen Wang, Martin Rünz, Lourdes Agapito
We propose DSP-SLAM, an object-oriented SLAM system that builds a rich and accurate joint map of dense 3D models for foreground objects, and sparse landmark points to represent the…
Multi-person Implicit Reconstruction from a Single Image
Armin Mustafa, Akin Caliskan, Lourdes Agapito +1
We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image. Existing multi-person methods suffe…
SelfPose: 3D Egocentric Pose Estimation from a Headset Mounted Camera
Denis Tome, Thiemo Alldieck, Patrick Peluse +4
We present a solution to egocentric 3D body pose estimation from monocular images captured from downward looking fish-eye cameras installed on the rim of a head mounted VR device.…
S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency
Mel Vecerik, Jean-Baptiste Regli, Oleg Sushkov +7
A robot's ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize general-purpose training criteria,…
DiverseNet: When One Right Answer is not Enough
Michael Firman, Neill D. F. Campbell, Lourdes Agapito +1
Many structured prediction tasks in machine vision have a collection of acceptable answers, instead of one definitive ground truth answer. Segmentation of images, for example, is s…