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.
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driv- ing: Datasets, Methods, and Challenges
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Introduces an early-exit mechanism in YOLOv8 that uses inter-vessel distance and closing speed from trajectories to adapt computation depth per frame in maritime scenes.
A new CNN architecture for LiDAR semantic labeling achieves higher cross-sensor portability with a reported 10 percentage point IoU gain over a reference method.
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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Trajectory-Aware Adaptive Inference in Object Detection Models
Introduces an early-exit mechanism in YOLOv8 that uses inter-vessel distance and closing speed from trajectories to adapt computation depth per frame in maritime scenes.
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Analyzing the Cross-Sensor Portability of Neural Network Architectures for LiDAR-based Semantic Labeling
A new CNN architecture for LiDAR semantic labeling achieves higher cross-sensor portability with a reported 10 percentage point IoU gain over a reference method.