Search Results for author:
Found 7 papers, 3 papers with code
PennSyn2Real: Training Object Recognition Models without Human Labeling
Scalable training data generation is a critical problem in deep learning.
Depth Completion via Deep Basis Fitting
In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements.
MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks
Real-time semantic image segmentation on platforms subject to size, weight and power (SWaP) constraints is a key area of interest for air surveillance and inspection.
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance
Depth estimation is an important capability for autonomous vehicles to understand and reconstruct 3D environments as well as avoid obstacles during the execution.
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image.
U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification
Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures.
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring.
Ranked #4 on
Homography Estimation
on S-COCO
