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Found 4 papers, 0 papers with code
Sample-Specific Output Constraints for Neural Networks
For illustration purposes, we model ConstraintNet by modifying a CNN and construct constraints for facial landmark prediction tasks.
Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling
In this paper we propose a new CNN architecture for the point-wise semantic labeling of LiDAR data which achieves state-of-the-art results while increasing portability across sensor types.
Improved Semantic Stixels via Multimodal Sensor Fusion
Our results indicate that the proposed mid-level fusion of LiDAR and camera data improves both the geometric and semantic accuracy of the Stixel model significantly while reducing the computational overhead as well as the amount of generated data in comparison to using a single modality on its own.
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation
The effectiveness of the proposed network architecture as well as the automated data generation process is demonstrated on a manually annotated ground truth dataset.
