Search Results for author:
Found 16 papers, 5 papers with code
GasMono: Geometry-Aided Self-Supervised Monocular Depth Estimation for Indoor Scenes
This paper tackles the challenges of self-supervised monocular depth estimation in indoor scenes caused by large rotation between frames and low texture.
CMDA: Cross-Modality Domain Adaptation for Nighttime Semantic Segmentation
However, limited by the low dynamic range of conventional cameras, images fail to capture structural details and boundary information in low-light conditions.
Causal reasoning in typical computer vision tasks
Deep learning has revolutionized the field of artificial intelligence.
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
The Monocular Depth Estimation Challenge
This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset.
Towards Generalization on Real Domain for Single Image Dehazing via Meta-Learning
In contrast, we present a domain generalization framework based on meta-learning to dig out representative and discriminative internal properties of real hazy domains without test-time training.
Visual Semantic Segmentation Based on Few/Zero-Shot Learning: An Overview
Specifically, the preliminaries on few/zero-shot visual semantic segmentation, including the problem definitions, typical datasets, and technical remedies, are briefly reviewed and discussed.
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth labels for training.
Ranked #1 on
Monocular Depth Estimation
on KITTI
Learn to Adapt for Monocular Depth Estimation
To boost the transferability of depth estimation models, we propose an adversarial depth estimation task and train the model in the pipeline of meta-learning.
Unsupervised Monocular Depth Estimation in Highly Complex Environments
Meanwhile, we further tackle the effects of unstable image transfer quality on domain adaptation, and an image adaptation approach is proposed to evaluate the quality of transferred images and re-weight the corresponding losses, so as to improve the performance of the adapted depth model.
Multi-task GANs for Semantic Segmentation and Depth Completion with Cycle Consistency
Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving.
Masked GANs for Unsupervised Depth and Pose Prediction with Scale Consistency
This paper proposes a masked generative adversarial network (GAN) for unsupervised monocular depth and ego-motion estimation. The MaskNet and Boolean mask scheme are designed in this framework to eliminate the effects of occlusions and impacts of visual field changes on the reconstruction loss and adversarial loss, respectively.
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and robotic manipulation.
Monocular Depth Estimation Based On Deep Learning: An Overview
With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy.
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey
Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks.
Deep Direct Visual Odometry
Extensive experiments on the KITTI dataset show that the proposed constraints can effectively improve the scale-consistency of TrajNet when compared with previous unsupervised monocular methods, and integration with TrajNet makes the initialization and tracking of DSO more robust and accurate.
