no code implementations • 18 Apr 2024 • Spencer Carmichael, Rahul Agrawal, Ram Vasudevan, Katherine A. Skinner
Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers.
no code implementations • 28 Mar 2024 • Lingjun Zhao, Jingyu Song, Katherine A. Skinner
Alternatively, camera and radar are commonly deployed on vehicles already on the road today, but performance of Camera-Radar (CR) fusion falls behind LC fusion.
no code implementations • 18 Feb 2024 • Jingyu Song, Lingjun Zhao, Katherine A. Skinner
We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors.
1 code implementation • 24 Jan 2024 • Spencer Carmichael, Austin Buchan, Mani Ramanagopal, Radhika Ravi, Ram Vasudevan, Katherine A. Skinner
Conventional cameras employed in autonomous vehicle (AV) systems support many perception tasks, but are challenged by low-light or high dynamic range scenes, adverse weather, and fast motion.
no code implementations • 2 Oct 2023 • Advaith Venkatramanan Sethuraman, Katherine A. Skinner
In this paper, we address the problem of sim-to-real transfer for object segmentation when there is no access to real examples of an object of interest during training, i. e. zero-shot sim-to-real transfer for segmentation.
no code implementations • 27 Sep 2022 • Advaith Venkatramanan Sethuraman, Manikandasriram Srinivasan Ramanagopal, Katherine A. Skinner
Underwater imaging is a critical task performed by marine robots for a wide range of applications including aquaculture, marine infrastructure inspection, and environmental monitoring.
no code implementations • 2 Sep 2022 • Alexandra Carlson, Manikandasriram Srinivasan Ramanagopal, Nathan Tseng, Matthew Johnson-Roberson, Ram Vasudevan, Katherine A. Skinner
Recent advances in neural radiance fields (NeRFs) achieve state-of-the-art novel view synthesis and facilitate dense estimation of scene properties.
no code implementations • 21 Feb 2021 • Kento Tomita, Katherine A. Skinner, Koki Ho
However, performance for these methods can degrade for input DEMs with increased sensor noise.
no code implementations • 29 Jul 2020 • Abigail R. Azari, John B. Biersteker, Ryan M. Dewey, Gary Doran, Emily J. Forsberg, Camilla D. K. Harris, Hannah R. Kerner, Katherine A. Skinner, Andy W. Smith, Rashied Amini, Saverio Cambioni, Victoria Da Poian, Tadhg M. Garton, Michael D. Himes, Sarah Millholland, Suranga Ruhunusiri
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets.
no code implementations • 17 Sep 2018 • Alexandra Carlson, Katherine A. Skinner, Ram Vasudevan, Matthew Johnson-Roberson
This domain shift is especially exaggerated between synthetic and real datasets.
no code implementations • 13 Sep 2018 • Jun-ming Zhang, Katherine A. Skinner, Ram Vasudevan, Matthew Johnson-Roberson
Initial disparity estimates are refined with an embedding learned from the semantic segmentation branch of the network.
1 code implementation • 21 Mar 2018 • Alexandra Carlson, Katherine A. Skinner, Ram Vasudevan, Matthew Johnson-Roberson
Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes.
1 code implementation • 23 Feb 2017 • Jie Li, Katherine A. Skinner, Ryan M. Eustice, Matthew Johnson-Roberson
Due to the depth-dependent water column effects inherent to underwater environments, we show that our end-to-end network implicitly learns a coarse depth estimate of the underwater scene from monocular underwater images.