16 citations · 28 across the 14 of their papers we have counts for
6 papers · 1 filter
EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervision
Jiahui Lei, Congyue Deng, Karl Schmeckpeper +2
We introduce Equivariant Neural Field Expectation Maximization (EFEM), a simple, effective, and robust geometric algorithm that can segment objects in 3D scenes without annotations…
Semantic keypoint-based pose estimation from single RGB frames
Karl Schmeckpeper, Philip R. Osteen, Yufu Wang +6
This paper presents an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional…
Cross-modal Map Learning for Vision and Language Navigation
Georgios Georgakis, Karl Schmeckpeper, Karan Wanchoo +4
We consider the problem of Vision-and-Language Navigation (VLN). The majority of current methods for VLN are trained end-to-end using either unstructured memory such as LSTM, or us…
Learning to Map for Active Semantic Goal Navigation
Georgios Georgakis, Bernadette Bucher, Karl Schmeckpeper +2
We consider the problem of object goal navigation in unseen environments. Solving this problem requires learning of contextual semantic priors, a challenging endeavour given the sp…
Object-centric Video Prediction without Annotation
Karl Schmeckpeper, Georgios Georgakis, Kostas Daniilidis
In order to interact with the world, agents must be able to predict the results of the world's dynamics. A natural approach to learn about these dynamics is through video predictio…
Deformable Linear Object Prediction Using Locally Linear Latent Dynamics
Wenbo Zhang, Karl Schmeckpeper, Pratik Chaudhari +1
We propose a framework for deformable linear object prediction. Prediction of deformable objects (e.g., rope) is challenging due to their non-linear dynamics and infinite-dimension…