Search Results for author:
Found 13 papers, 3 papers with code
SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and Opposing Viewpoints
Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers.
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation
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.
LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors.
Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception
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.
STARS: Zero-shot Sim-to-Real Transfer for Segmentation of Shipwrecks in Sonar Imagery
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.
WaterNeRF: Neural Radiance Fields for Underwater Scenes
Underwater imaging is a critical task performed by marine robots for a wide range of applications including aquaculture, marine infrastructure inspection, and environmental monitoring.
CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations
Recent advances in neural radiance fields (NeRFs) achieve state-of-the-art novel view synthesis and facilitate dense estimation of scene properties.
Bayesian Deep Learning for Segmentation for Autonomous Safe Planetary Landing
However, performance for these methods can degrade for input DEMs with increased sensor noise.
Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade
Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets.
Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation
This domain shift is especially exaggerated between synthetic and real datasets.
DispSegNet: Leveraging Semantics for End-to-End Learning of Disparity Estimation from Stereo Imagery
Initial disparity estimates are refined with an embedding learned from the semantic segmentation branch of the network.
Modeling Camera Effects to Improve Visual Learning from Synthetic Data
Recent work has focused on generating synthetic imagery to increase the size and variability of training data for learning visual tasks in urban scenes.
WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images
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.
