arXiv:2605.20301v1 Announce Type: cross Abstract: In autonomous driving, 3D object detection is essential for accurate perception and reliable decision-making. However, object motion and ego-motion often induce cross-frame spatiotemporal inconsistencies in BEV-based detectors, leading to temporal BEV feature misalignment and degraded spatiotemporal consistency. To address these challenges, we propose Co-Fusion4D, a unified framework that explicitly preserves cross-frame spatiotemporal consistency and suppresses temporal feature drift. Co-Fusion4D adopts a current-frame-centric strategy, treati
The increasing sophistication of autonomous systems, particularly in self-driving cars, necessitates more robust 3D perception solutions to handle real-world complexities like object motion.
Improved 3D object detection is critical for the reliability and safety of autonomous vehicles, directly impacting their deployment and public acceptance.
This research introduces a method to overcome current limitations in spatio-temporal consistency for 3D object detection, enhancing performance in dynamic environments.
- · Autonomous vehicle developers
- · Robotics companies
- · Computer vision researchers
- · AI hardware manufacturers
- · Companies with less sophisticated 3D perception systems
More reliable and safer operation of autonomous vehicles in complex scenarios.
Accelerated development and commercialization of self-driving technology across various sectors, including logistics and personal transportation.
Reduced regulatory hurdles for autonomous systems due to enhanced safety and predictability.
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Read at arXiv cs.AI