5 accepted papers
Applying pretraining-finetuning paradigm to event cameras presents significant challenges due to the scarcity of large-scale event datasets and the inherently sparse nature of event data, which increases the risk of overfitting during extensive pretraining.In this paper, we explore the transfer of p…
Fully sparse 3D detectors have recently gained significant attention due to their efficiency in long-range detection. However, sparse 3D detectors extract features only from non-empty voxels, which impairs long-range interactions and causes the center feature missing. The former weakens the feature…
Multi-sensor fusion is crucial for improving the performance and robustness of end-to-end autonomous driving systems. Existing methods predominantly adopt either attention-based flatten fusion or bird’s eye view fusion through geometric transformations. However, these approaches often suffer from li…
Event Stream Super-Resolution (ESR) aims to address the challenge of insufficient spatial resolution in event streams which holds great significance for the application of event cameras in complex scenarios. Previous works for ESR often process positive and negative events in a mixed paradigm. This…
Current Event Stream Super-Resolution (ESR) methods overlook the redundant and complementary information present in positive and negative events within the event stream, employing a direct mixing approach for super-resolution, which may lead to detail loss and inefficiency. To address these issues,…