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Found 7 papers, 0 papers with code
SLIM: Self-Supervised LiDAR Scene Flow and Motion Segmentation
Recently, several frameworks for self-supervised learning of 3D scene flow on point clouds have emerged.
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.
Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling
To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework.
Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles
The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery.
Semantically Guided Depth Upsampling
We present a novel method for accurate and efficient up- sampling of sparse depth data, guided by high-resolution imagery.
