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Found 6 papers, 2 papers with code
VoxelTrack: Exploring Voxel Representation for 3D Point Cloud Object Tracking
Current LiDAR point cloud-based 3D single object tracking (SOT) methods typically rely on point-based representation network.
Towards Category Unification of 3D Single Object Tracking on Point Clouds
Based on this observation, we design a novel point set representation learning network inheriting transformer architecture, termed AdaFormer, which adaptively encodes the dynamically varying shape and size information from cross-category data in a unified manner.
GLT-T++: Global-Local Transformer for 3D Siamese Tracking with Ranking Loss
Incorporating this transformer-based voting scheme into 3D RPN, a novel Siamese method dubbed GLT-T is developed for 3D single object tracking on point clouds.
Dual-space Compressed Sensing
Here we propose an alternate CS regime in situations where the image can be sampled in two incoherent spaces simultaneously, with a special focus on image sampling in Fourier reciprocal spaces (e. g. real-space and k-space).
LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network
In this paper, we propose a novel online self-calibration approach for Light Detection and Ranging (LiDAR) and camera sensors.
Semantic Flow-guided Motion Removal Method for Robust Mapping
In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions.
