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Davy Neven
Researcher at Katholieke Universiteit Leuven
Publications - 17
Citations - 1912
Davy Neven is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 12, co-authored 17 publications receiving 1158 citations.
Papers
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Proceedings ArticleDOI
Towards End-to-End Lane Detection: an Instance Segmentation Approach
TL;DR: In this article, the authors cast the lane detection problem as an instance segmentation problem, in which each lane forms its own instance and parametrize the segmented lane instances before fitting the lane, in contrast to a fixed "bird's-eye view" transformation.
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Semantic Instance Segmentation with a Discriminative Loss Function
TL;DR: This work proposes an approach of combining an off-the-shelf network with a principled loss function inspired by a metric learning objective that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.
Proceedings ArticleDOI
Sparse and Noisy LiDAR Completion with RGB Guidance and Uncertainty
TL;DR: In this article, the authors proposed a new depth completion framework which extracts both global and local information in order to produce proper depth maps, and further proposed a fusion method with RGB guidance from a monocular camera to leverage object information and to correct mistakes in the sparse input.
Proceedings ArticleDOI
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth
TL;DR: In this article, the spatial embeddings of pixels belonging to the same instance are jointly learned to maximize the intersection-over-union of the resulting instance mask, which achieves state-of-the-art performance on the Cityscapes benchmark.
Proceedings ArticleDOI
Semantic Instance Segmentation for Autonomous Driving
TL;DR: This work proposes a discriminative loss function, operating at pixel level, that encourages a convolutional network to produce a representation of the image that can easily be clustered into instances with a simple post-processing step.