Depth as Points: Center Point-based Depth Estimation
This work addresses the need for efficient perception in autonomous driving, offering a method that reduces computational demands while improving performance, though it appears incremental in its approach.
The paper tackles efficient depth estimation for autonomous driving by proposing CenterDepth, a lightweight architecture that integrates global semantic information and multi-scale features, achieving superior computational speed and prediction accuracy.
The perception of vehicles and pedestrians in urban scenarios is crucial for autonomous driving. This process typically involves complicated data collection, imposes high computational and hardware demands. To address these limitations, we first develop a highly efficient method for generating virtual datasets, which enables the creation of task- and scenario-specific datasets in a short time. Leveraging this method, we construct the virtual depth estimation dataset VirDepth, a large-scale, multi-task autonomous driving dataset. Subsequently, we propose CenterDepth, a lightweight architecture for monocular depth estimation that ensures high operational efficiency and exhibits superior performance in depth estimation tasks with highly imbalanced height-scale distributions. CenterDepth integrates global semantic information through the innovative Center FC-CRFs algorithm, aggregates multi-scale features based on object key points, and enables detection-based depth estimation of targets. Experiments demonstrate that our proposed method achieves superior performance in terms of both computational speed and prediction accuracy.