Search Results for author:
Found 7 papers, 1 papers with code
HSPFormer: Hierarchical Spatial Perception Transformer for Semantic Segmentation
To address this issue, we propose a novel architecture, the Hierarchical Spatial Perception Transformer (HSPFormer), which integrates monocular depth estimation and semantic segmentation into a unified framework for the first time.
Ranked #3 on
Semantic Segmentation
on KITTI-360
LOKI: A Comprehensive Synthetic Data Detection Benchmark using Large Multimodal Models
With the rapid development of AI-generated content, the future internet may be inundated with synthetic data, making the discrimination of authentic and credible multimodal data increasingly challenging.
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.
