We present a simple approach that uses a variational loss to enforce calibration in probabilistic regression networks.
We present a large-scale benchmark and performant approaches for long-horizon task planning over large 3D scene graphs
We present a weakly supervised approach that reconstructs objects in a canonical coordinate space.
Differentiable models of time-varying dynamics and image formation pipelines result in highly accurate physical parameter estimation from video
We present a dataset and introduce a new benchmark for *amodal* layout estimation from monocular imagery.
We present a survey of current probabilistic object detection techniques, and identify promising avenues for further research.
We present end-to-end differentiable dense SLAM systems that open up new possibilites for integrating deep learning and SLAM.
MapLite is a one-click autonomous navigation system for a vehicle that only uses OpenStreetMap data and local sensing (**Best paper award, RAL 2019**).
We present a neural network that "hallucinates" the layout of a road scene from a single image, including scene parts that are outside the bounds of the image.
We present a monocular object SLAM system that tracks not just the camera, but also other moving objects in the scene.