no code implementations • 29 Sep 2023 • Anthony Hu, Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton, Gianluca Corrado
Autonomous driving promises transformative improvements to transportation, but building systems capable of safely navigating the unstructured complexity of real-world scenarios remains challenging.
1 code implementation • 14 Jul 2023 • Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan, Jamie Shotton, Alex Kendall, Claire Tomlin, Christopher P. Burgess
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features.
1 code implementation • 14 Oct 2022 • Anthony Hu, Gianluca Corrado, Nicolas Griffiths, Zak Murez, Corina Gurau, Hudson Yeo, Alex Kendall, Roberto Cipolla, Jamie Shotton
Our approach is the first camera-only method that models static scene, dynamic scene, and ego-behaviour in an urban driving environment.
1 code implementation • ICCV 2021 • Anthony Hu, Zak Murez, Nikhil Mohan, Sofía Dudas, Jeffrey Hawke, Vijay Badrinarayanan, Roberto Cipolla, Alex Kendall
We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras.
Ranked #1 on Bird's-Eye View Semantic Segmentation on nuScenes (IoU veh - 224x480 - No vis filter - 100x50 at 0.25 metric)
1 code implementation • ECCV 2020 • Zak Murez, Tarrence van As, James Bartolozzi, Ayan Sinha, Vijay Badrinarayanan, Andrew Rabinovich
Traditional approaches to 3D reconstruction rely on an intermediate representation of depth maps prior to estimating a full 3D model of a scene.
Ranked #1 on Depth Estimation on ScanNet
1 code implementation • ECCV 2020 • Ayan Sinha, Zak Murez, James Bartolozzi, Vijay Badrinarayanan, Andrew Rabinovich
Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the accuracy of MVS systems.
Ranked #1 on Depth Estimation on ScanNetV2
no code implementations • 10 Jun 2019 • Mohammad Rostami, Soheil Kolouri, Zak Murez, Yuri Owekcho, Eric Eaton, Kuyngnam Kim
Zero-shot learning (ZSL) is a framework to classify images belonging to unseen classes based on solely semantic information about these unseen classes.
no code implementations • CVPR 2018 • Zak Murez, Soheil Kolouri, David Kriegman, Ravi Ramamoorthi, Kyungnam Kim
This is achieved by adding extra networks and losses that help regularize the features extracted by the backbone encoder network.
no code implementations • ICCV 2015 • Zak Murez, Tali treibitz, Ravi Ramamoorthi, David Kriegman
Next, we model the blur due to scattering of light from the object.