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SVQNet: Sparse Voxel-Adjacent Query Network for 4D Spatio-Temporal LiDAR Semantic Segmentation
To take full advantage of the historical frames high-efficiently, we shunt the historical points into two groups with reference to the current points.
From One to Many: Dynamic Cross Attention Networks for LiDAR and Camera Fusion
The whole fusion architecture named Dynamic Cross Attention Network (DCAN) exploits multi-level image features and adapts to multiple representations of point clouds, which allows DCA to serve as a plug-in fusion module.
Sparse Cross-scale Attention Network for Efficient LiDAR Panoptic Segmentation
Two major challenges of 3D LiDAR Panoptic Segmentation (PS) are that point clouds of an object are surface-aggregated and thus hard to model the long-range dependency especially for large instances, and that objects are too close to separate each other.
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