450 citations · 1.1k across the 20 of their papers we have counts for
22 papers
Linking vision and motion for self-supervised object-centric perception
Kaylene C. Stocking, Zak Murez, Vijay Badrinarayanan +4
Object-centric representations enable autonomous driving algorithms to reason about interactions between many independent agents and scene features. Traditionally these representat…
Reimagining an autonomous vehicle
Jeffrey Hawke, Haibo E, Vijay Badrinarayanan +1
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…
FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras
Anthony Hu, Zak Murez, Nikhil Mohan +5
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…
MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality
Zhengyang Wu, Srivignesh Rajendran, Tarrence van As +3
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 com…
Scan2Plan: Efficient Floorplan Generation from 3D Scans of Indoor Scenes
Ameya Phalak, Vijay Badrinarayanan, Andrew Rabinovich
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…
Atlas: End-to-End 3D Scene Reconstruction from Posed Images
Zak Murez, Tarrence van As, James Bartolozzi +3
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 approac…
DELTAS: Depth Estimation by Learning Triangulation And densification of Sparse points
Ayan Sinha, Zak Murez, James Bartolozzi +2
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 3…
EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding
Zhengyang Wu, Srivignesh Rajendran, Tarrence van As +3
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, m…
DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
Ameya Phalak, Zhao Chen, Darvin Yi +3
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 o…
Gradient Adversarial Training of Neural Networks
Ayan Sinha, Zhao Chen, Vijay Badrinarayanan +1
We propose gradient adversarial training, an auxiliary deep learning framework applicable to different machine learning problems. In gradient adversarial training, we leverage a pr…
Estimating Depth from RGB and Sparse Sensing
Zhao Chen, Vijay Badrinarayanan, Gilad Drozdov +1
We present a deep model that can accurately produce dense depth maps given an RGB image with known depth at a very sparse set of pixels. The model works simultaneously for both ind…
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee +1
Deep multitask networks, in which one neural network produces multiple predictive outputs, can offer better speed and performance than their single-task counterparts but are challe…
RoomNet: End-to-End Room Layout Estimation
Chen-Yu Lee, Vijay Badrinarayanan, Tomasz Malisiewicz +1
This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceilin…
Deep Cuboid Detection: Beyond 2D Bounding Boxes
Debidatta Dwibedi, Tomasz Malisiewicz, Vijay Badrinarayanan +1
We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches wh…
SceneNet: Understanding Real World Indoor Scenes With Synthetic Data
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan +2
Scene understanding is a prerequisite to many high level tasks for any automated intelligent machine operating in real world environments. Recent attempts with supervised learning…
TemplateNet for Depth-Based Object Instance Recognition
Ujwal Bonde, Vijay Badrinarayanan, Roberto Cipolla +1
We present a novel deep architecture termed templateNet for depth based object instance recognition. Using an intermediate template layer we exploit prior knowledge of an object's…
Symmetry-invariant optimization in deep networks
Vijay Badrinarayanan, Bamdev Mishra, Roberto Cipolla
Recent works have highlighted scale invariance or symmetry that is present in the weight space of a typical deep network and the adverse effect that it has on the Euclidean gradien…
Understanding symmetries in deep networks
Vijay Badrinarayanan, Bamdev Mishra, Roberto Cipolla
Recent works have highlighted scale invariance or symmetry present in the weight space of a typical deep network and the adverse effect it has on the Euclidean gradient based stoch…
Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding
Alex Kendall, Vijay Badrinarayanan, Roberto Cipolla
We present a deep learning framework for probabilistic pixel-wise semantic segmentation, which we term Bayesian SegNet. Semantic segmentation is an important tool for visual scene…
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Vijay Badrinarayanan, Alex Kendall, Roberto Cipolla
We present a novel and practical deep fully convolutional neural network architecture for semantic pixel-wise segmentation termed SegNet. This core trainable segmentation engine co…
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling
Vijay Badrinarayanan, Ankur Handa, Roberto Cipolla
We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully…
SynthCam3D: Semantic Understanding With Synthetic Indoor Scenes
Ankur Handa, Viorica Patraucean, Vijay Badrinarayanan +2
We are interested in automatic scene understanding from geometric cues. To this end, we aim to bring semantic segmentation in the loop of real-time reconstruction. Our semantic seg…