nuScenes provides the first public autonomous-driving dataset that includes synchronized 360-degree data from cameras, radars, and lidar together with 3D bounding-box annotations across 1000 scenes.
PointRNN: Point recurrent neural network for moving point cloud processing
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
STS-Mixer decomposes 4D point cloud videos into multi-band spectral signals via graph transforms and mixes them with spatiotemporal representations to achieve better results on 3D action recognition and 4D semantic segmentation benchmarks.
TARS jointly performs object detection and radar scene flow estimation by building a Traffic Vector Field from detector features to enforce traffic-level rigid motion consistency, reporting 23% and 15% gains on proprietary and View-of-Delft datasets.
citing papers explorer
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nuScenes: A multimodal dataset for autonomous driving
nuScenes provides the first public autonomous-driving dataset that includes synchronized 360-degree data from cameras, radars, and lidar together with 3D bounding-box annotations across 1000 scenes.
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STS-Mixer: Spatio-Temporal-Spectral Mixer for 4D Point Cloud Video Understanding
STS-Mixer decomposes 4D point cloud videos into multi-band spectral signals via graph transforms and mixes them with spatiotemporal representations to achieve better results on 3D action recognition and 4D semantic segmentation benchmarks.
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TARS: Traffic-Aware Radar Scene Flow Estimation
TARS jointly performs object detection and radar scene flow estimation by building a Traffic Vector Field from detector features to enforce traffic-level rigid motion consistency, reporting 23% and 15% gains on proprietary and View-of-Delft datasets.