Search Results for author:
Found 9 papers, 3 papers with code
Stereo-Matching Knowledge Distilled Monocular Depth Estimation Filtered by Multiple Disparity Consistency
In stereo-matching knowledge distillation methods of the self-supervised monocular depth estimation, the stereo-matching network's knowledge is distilled into a monocular depth network through pseudo-depth maps.
Modeling Stereo-Confidence Out of the End-to-End Stereo-Matching Network via Disparity Plane Sweep
We propose a novel stereo-confidence that can be measured externally to various stereo-matching networks, offering an alternative input modality choice of the cost volume for learning-based approaches, especially in safety-critical systems.
Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations
Anomaly detection is a critical and challenging task that aims to identify data points deviating from normal patterns and distributions within a dataset.
SimCol3D -- 3D Reconstruction during Colonoscopy Challenge
We show that depth prediction in virtual colonoscopy is robustly solvable, while pose estimation remains an open research question.
Lightweight Monocular Depth Estimation via Token-Sharing Transformer
While most lightweight monocular depth estimation methods have been developed using convolution neural networks, the Transformer has been gradually utilized in monocular depth estimation recently.
Fix the Noise: Disentangling Source Feature for Controllable Domain Translation
This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model.
I See-Through You: A Framework for Removing Foreground Occlusion in Both Sparse and Dense Light Field Images
To apply LF-DeOcc in both LF datasets, we propose a framework, ISTY, which is defined and divided into three roles: (1) extract LF features, (2) define the occlusion, and (3) inpaint occluded regions.
Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN
Owing to the disentangled feature space, our method can smoothly control the degree of the source features in a single model.
Cyclic Test Time Augmentation with Entropy Weight Method
The policy search method with the best level of input data dependency involves training a loss predictor network to estimate suitable transformations for each of the given input image in independent manner, resulting in instance-level transformation extraction.
