C
Clément Godard
Researcher at University College London
Publications - 15
Citations - 4782
Clément Godard 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 7, co-authored 11 publications receiving 2945 citations.
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.
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.
Posted Content
Unsupervised Monocular Depth Estimation with Left-Right Consistency
TL;DR: This paper proposes a novel training objective that enables the convolutional neural network to learn to perform single image depth estimation, despite the absence of ground truth depth data, and produces state of the art results for monocular depth estimation on the KITTI driving dataset.
Book ChapterDOI
Deep Burst Denoising
TL;DR: In this paper, a recurrent fully convolutional deep neural network (CNN) is proposed to integrate multiple short (thus noisy) frames in a burst and intelligently integrate the content, thus avoiding the above downsides.