3 papers
cs.CV2024
SAda-Net: A Self-Supervised Adaptive Stereo Estimation CNN For Remote Sensing Image Data
Dominik Hirner, Friedrich Fraundorfer
Stereo estimation has made many advancements in recent years with the introduction of deep-learning. However the traditional supervised approach to deep-learning requires the creat…
cs.CV2022
FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion
Dominik Hirner, Friedrich Fraundorfer
We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity netwo…
cs.CV2020
FC-DCNN: A densely connected neural network for stereo estimation
Dominik Hirner, Friedrich Fraundorfer
We propose a novel lightweight network for stereo estimation. Our network consists of a fully-convolutional densely connected neural network (FC-DCNN) that computes matching costs…