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Found 7 papers, 3 papers with code
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-Supervision
In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision.
TridentAdapt: Learning Domain-invariance via Source-Target Confrontation and Self-induced Cross-domain Augmentation
Due to the difficulty of obtaining ground-truth labels, learning from virtual-world datasets is of great interest for real-world applications like semantic segmentation.
Looking Beyond Corners: Contrastive Learning of Visual Representations for Keypoint Detection and Description Extraction
Learnable keypoint detectors and descriptors are beginning to outperform classical hand-crafted feature extraction methods.
Learnable Exposure Fusion for Dynamic Scenes
To the best of our knowledge, no efficient and robust CNN-based end-to-end approach can be found in the literature for this kind of problem.
Monocular Depth Estimation by Learning from Heterogeneous Datasets
Depth estimation provides essential information to perform autonomous driving and driver assistance.
Ranked #44 on
Monocular Depth Estimation
on KITTI Eigen split
Multimodal End-to-End Autonomous Driving
On the other hand, we find end-to-end driving approaches that try to learn a direct mapping from input raw sensor data to vehicle control signals.
Real-Time Lane ID Estimation Using Recurrent Neural Networks With Dual Convention
We interpret this task as a classification problem by limiting the maximum number of lane candidates to eight.
