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Gabriel J. Brostow
Researcher at University College London
Publications - 114
Citations - 12823
Gabriel J. Brostow is an academic researcher from University College London. The author has contributed to research in topics: Computer science & Ground truth. The author has an hindex of 39, co-authored 107 publications receiving 9118 citations. Previous affiliations of Gabriel J. Brostow include Georgia Institute of Technology & University of Cambridge.
Papers
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Proceedings ArticleDOI
Unsupervised Monocular Depth Estimation with Left-Right Consistency
TL;DR: In this article, the authors propose a novel training objective that enables CNNs to learn to perform single image depth estimation, despite the absence of ground truth depth data, by generating disparity images by training their network with an image reconstruction loss.
Journal ArticleDOI
Semantic object classes in video: A high-definition ground truth database
TL;DR: The Cambridge-driving Labeled Video Database (CamVid) is presented as the first collection of videos with object class semantic labels, complete with metadata, and the relevance of the database is evaluated by measuring the performance of an algorithm from each of three distinct domains: multi-class object recognition, pedestrian detection, and label propagation.
Book ChapterDOI
Segmentation and Recognition Using Structure from Motion Point Clouds
TL;DR: This work proposes an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion that works well on sparse, noisy point clouds, and unlike existing approaches, does not need appearance-based descriptors.
Proceedings ArticleDOI
Digging Into Self-Supervised Monocular Depth Estimation
TL;DR: In this paper, the authors propose a set of improvements, which together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods, and demonstrate the effectiveness of each component in isolation, and show high quality, state-of-theart results on the KITTI benchmark.
Posted Content
Digging Into Self-Supervised Monocular Depth Estimation
TL;DR: It is shown that a surprisingly simple model, and associated design choices, lead to superior predictions, and together result in both quantitatively and qualitatively improved depth maps compared to competing self-supervised methods.