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Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image.
PointNLM: Point Nonlocal-Means for vegetation segmentation based on middle echo point clouds
Second, to accurately extract trees from all point clouds, we propose a 3D deep learning network, PointNLM, to semantically segment tree crowns.
NeuroTreeNet: A New Method to Explore Horizontal Expansion Network
It is widely recognized that the deeper networks or networks with more feature maps have better performance.
Geometric Multi-Model Fitting by Deep Reinforcement Learning
This paper deals with the geometric multi-model fitting from noisy, unstructured point set data (e. g., laser scanned point clouds).
LiDAR-Video Driving Dataset: Learning Driving Policies Effectively
Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision.
