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20162024
most citedWaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images

397 citations · 1k across the 44 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

physics.ins-det2017

Low cost underwater acoustic localization

Eduardo Iscar, Atulya Shree, Nicholas Goumas +1

Over the course of the last decade, the cost of marine robotic platforms has significantly decreased. In part this has lowered the barriers to entry of exploring and monitoring lar…

cs.MA2017

Robust Environmental Mapping by Mobile Sensor Networks

Hyongju Park, Jinsun Liu, Matthew Johnson-Roberson +1

Constructing a spatial map of environmental parameters is a crucial step to preventing hazardous chemical leakages, forest fires, or while estimating a spatially distributed physic…

cs.CV2017★ 96 cited

Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars

Manikandasriram Srinivasan Ramanagopal, Cyrus Anderson, Ram Vasudevan +1

One of the major open challenges in self-driving cars is the ability to detect cars and pedestrians to safely navigate in the world. Deep learning-based object detector approaches…

cs.RO2017★ 1 cited

Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting

Henry O. Jacobs, Owen K. Hughes, Matthew Johnson-Roberson +1

The success of autonomous systems will depend upon their ability to safely navigate human-centric environments. This motivates the need for a real-time, probabilistic forecasting a…

eess.SY2017

Safe Trajectory Synthesis for Autonomous Driving in Unforeseen Environments

Shreyas Kousik, Sean Vaskov, Matthew Johnson-Roberson +1

Path planning for autonomous vehicles in arbitrary environments requires a guarantee of safety, but this can be impractical to ensure in real-time when the vehicle is described wit…

cs.CV2017★ 397 cited

WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images

Jie Li, Katherine A. Skinner, Ryan M. Eustice +1

This paper reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline u…