Over the past years, numerous methods have emerged to automate the quantification of
animal behavior at a resolution not previously imaginable. This has opened up a new field of …

We present iNeRF, a framework that performs mesh-free pose estimation by" inverting" a
Neural Radiance Field (NeRF). NeRFs have been shown to be remarkably effective for the …

Detecting objects and estimating their viewpoints in images are key tasks of 3D scene
understanding. Recent approaches have achieved excellent results on very large …

In this work, we present a novel data-driven method for robust 6DoF object pose estimation
from a single RGBD image. Unlike previous methods that directly regressing pose …

A key technical challenge in performing 6D object pose estimation from RGB-D image is to
fully leverage the two complementary data sources. Prior works either extract information …

We present a new framework for semantic segmentation without annotations via clustering.
Off-the-shelf clustering methods are limited to curated, single-label, and object-centric …

We present a simple, flexible, and general framework titled Partial Registration Network
(PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning …

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide
range of manipulation skills. However, many of the more complex behaviors are also …

Using synthetic data for training deep neural networks for robotic manipulation holds the
promise of an almost unlimited amount of pre-labeled training data, generated safely out of …

We present a method for learning a generative 3D model based on neural radiance fields,
trained solely from data with only single views of each object. While generating realistic …