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20162022
most citedSemantic Instance Segmentation with a Discriminative Loss Function

444 citations · 1.6k across the 80 of their papers we have counts for

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cs.CV2021

Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution

Andreas Lugmayr, Martin Danelljan, Fisher Yu +2

Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions. Yet, the dominant paradigm…

cs.CV2021

Neural Architecture Search for Efficient Uncalibrated Deep Photometric Stereo

Francesco Sarno, Suryansh Kumar, Berk Kaya +3

We present an automated machine learning approach for uncalibrated photometric stereo (PS). Our work aims at discovering lightweight and computationally efficient PS neural network…

cs.CV2021

Neural Radiance Fields Approach to Deep Multi-View Photometric Stereo

Berk Kaya, Suryansh Kumar, Francesco Sarno +2

We present a modern solution to the multi-view photometric stereo problem (MVPS). Our work suitably exploits the image formation model in a MVPS experimental setup to recover the d…

cs.CV2021

Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1

Autonomous navigation requires structured representation of the road network and instance-wise identification of the other traffic agents. Since the traffic scene is defined on the…

cs.CV20212 cited

Context-aware Padding for Semantic Segmentation

Yu-Hui Huang, Marc Proesmans, Luc Van Gool

Zero padding is widely used in convolutional neural networks to prevent the size of feature maps diminishing too fast. However, it has been claimed to disturb the statistics at the…

cs.CV2021

Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather

Martin Hahner, Christos Sakaridis, Dengxin Dai +1

This work addresses the challenging task of LiDAR-based 3D object detection in foggy weather. Collecting and annotating data in such a scenario is very time, labor and cost intensi…