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A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Unlike other LiDAR-based multi-task architectures, our proposed PAttFormer does not require separate feature encoders for multiple task-specific point cloud representations, resulting in a network that is 3x smaller and 1. 4x faster while achieving competitive performance on the nuScenes and KITTI benchmarks for autonomous driving perception.
Self-Supervised Multi-Object Tracking For Autonomous Driving From Consistency Across Timescales
We hypothesize that this drawback results from formulating self-supervised objectives that are limited to single frames or frame pairs.
Self-Supervised Representation Learning from Temporal Ordering of Automated Driving Sequences
Self-supervised feature learning enables perception systems to benefit from the vast raw data recorded by vehicle fleets worldwide.
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