Fine-Grained Multilevel Fusion for Anti-Occlusion Monocular 3D Object Detection

Abstract

We propose a deep fine-grained multi-level fusion architecture for monocular 3D object detection, with an additionally designed anti-occlusion optimization process. Conventional monocular 3D object detection methods usually leverage geometry constraints such as keypoints, object shape relationships, and 3D to 2D optimizations to offset the lack of accurate depth information. However, these methods still struggle against directly extracting rich information for fusion from the depth estimation. To solve the problem, we integrate the monocular 3D features with the pseudo-LiDAR filter generation network between fine-grained multi-level layers. Our network utilizes the inherent multi-scale and promotes depth and semantic information flow in different stages. The new architecture can obtain features that incorporate more reliable depth information. At the same time, the problem of occlusion among objects is prevalent in natural scenes yet remains unsolved mainly. We propose a novel loss function that aims at alleviating the problem of occlusion. Extensive experiments have proved that the framework demonstrates a competitive performance, especially for the complex scenes with occlusion.