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Found 9 papers, 3 papers with code
Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation
Here, we propose a UAV system for real-time semantic inference and fusion of multiple sensor modalities.
Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles
In this work, we propose a UAV system for real-time semantic inference and fusion of multiple sensor modalities.
LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices
Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images.
Real-time Multi-Adaptive-Resolution-Surfel 6D LiDAR Odometry using Continuous-time Trajectory Optimization
Simultaneous Localization and Mapping (SLAM) is an essential capability for autonomous robots, but due to high data rates of 3D LiDARs real-time SLAM is challenging.
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching
We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric.
LatticeNet: Fast Point Cloud Segmentation Using Permutohedral Lattices
Deep convolutional neural networks (CNNs) have shown outstanding performance in the task of semantically segmenting images.
Ranked #30 on
3D Semantic Segmentation
on SemanticKITTI
Semi-Supervised Semantic Mapping through Label Propagation with Semantic Texture Meshes
We propose to represent the semantic map as a geometrical mesh and a semantic texture coupled at independent resolution.
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Despite the relevance of semantic scene understanding for this application, there is a lack of a large dataset for this task which is based on an automotive LiDAR.
Ranked #32 on
3D Semantic Segmentation
on SemanticKITTI
Detection and Tracking of Small Objects in Sparse 3D Laser Range Data
Detection and tracking of dynamic objects is a key feature for autonomous behavior in a continuously changing environment.
