Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Badrinarayanan, Vijay"'
Autor:
Marcu, Ana-Maria, Chen, Long, Hünermann, Jan, Karnsund, Alice, Hanotte, Benoit, Chidananda, Prajwal, Nair, Saurabh, Badrinarayanan, Vijay, Kendall, Alex, Shotton, Jamie, Arani, Elahe, Sinavski, Oleg
We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving. The dataset contains 28K unique short video scenarios, and 419K annotations. Evaluating state-of-the-art vision-language models on our benchmark
Externí odkaz:
http://arxiv.org/abs/2312.14115
Autor:
Stocking, Kaylene C., Murez, Zak, Badrinarayanan, Vijay, Shotton, Jamie, Kendall, Alex, Tomlin, Claire, Burgess, Christopher P.
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representations have been obtained via supervised learning, but this decouples pe
Externí odkaz:
http://arxiv.org/abs/2307.07147
The self driving challenge in 2021 is this century's technological equivalent of the space race, and is now entering the second major decade of development. Solving the technology will create social change which parallels the invention of the automob
Externí odkaz:
http://arxiv.org/abs/2108.05805
Autor:
Hu, Anthony, Murez, Zak, Mohan, Nikhil, Dudas, Sofía, Hawke, Jeffrey, Badrinarayanan, Vijay, Cipolla, Roberto, Kendall, Alex
Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras. Our model predicts future instance s
Externí odkaz:
http://arxiv.org/abs/2104.10490
Autor:
Murez, Zak, van As, Tarrence, Bartolozzi, James, Sinha, Ayan, Badrinarayanan, Vijay, Rabinovich, Andrew
We present an end-to-end 3D reconstruction method for a scene by directly regressing a truncated signed distance function (TSDF) from a set of posed RGB images. Traditional approaches to 3D reconstruction rely on an intermediate representation of dep
Externí odkaz:
http://arxiv.org/abs/2003.10432
Multi-view stereo (MVS) is the golden mean between the accuracy of active depth sensing and the practicality of monocular depth estimation. Cost volume based approaches employing 3D convolutional neural networks (CNNs) have considerably improved the
Externí odkaz:
http://arxiv.org/abs/2003.08933
Autor:
Wu, Zhengyang, Rajendran, Srivignesh, van As, Tarrence, Zimmermann, Joelle, Badrinarayanan, Vijay, Rabinovich, Andrew
With the emergence of Virtual and Mixed Reality (XR) devices, eye tracking has received significant attention in the computer vision community. Eye gaze estimation is a crucial component in XR -- enabling energy efficient rendering, multi-focal displ
Externí odkaz:
http://arxiv.org/abs/2003.08806
We introduce Scan2Plan, a novel approach for accurate estimation of a floorplan from a 3D scan of the structural elements of indoor environments. The proposed method incorporates a two-stage approach where the initial stage clusters an unordered poin
Externí odkaz:
http://arxiv.org/abs/2003.07356
Autor:
Wu, Zhengyang, Rajendran, Srivignesh, van As, Tarrence, Zimmermann, Joelle, Badrinarayanan, Vijay, Rabinovich, Andrew
Eye gaze estimation and simultaneous semantic understanding of a user through eye images is a crucial component in Virtual and Mixed Reality; enabling energy efficient rendering, multi-focal displays and effective interaction with 3D content. In head
Externí odkaz:
http://arxiv.org/abs/1908.09060
Autor:
Phalak, Ameya, Chen, Zhao, Yi, Darvin, Gupta, Khushi, Badrinarayanan, Vijay, Rabinovich, Andrew
We present DeepPerimeter, a deep learning based pipeline for inferring a full indoor perimeter (i.e. exterior boundary map) from a sequence of posed RGB images. Our method relies on robust deep methods for depth estimation and wall segmentation to ge
Externí odkaz:
http://arxiv.org/abs/1904.11595