MonoKey: Keypoint-Based Monocular 3D Object Detection Using Prior Guidance for Occlusion Robustness

Abstract

Monocular 3D object detection has gained attention for its cost-efficiency and simpler setup compared to multi-sensor systems. In this task, accurate depth estimation is crucial for precise object localization, yet extracting sufficient depth cues from a single image remains inherently challenging. Moreover, when occlusions occur, structural cues become limited, making precise object localization even more difficult. To address these problems, we propose MonoKey, a keypoint-based monocular 3D object detection method robust to occlusion. MonoKey leverages 2D keypoints due to their suitability for recovering occluded regions. The occlusion-robust 2D keypoint detection approach estimates keypoints and reconstructs occluded ones by using prior information. The frequency-based global-local depth predictor estimates 3D cues using fast Fourier convolution to incorporate both global and local context. These 3D cues and keypoints are then fused in a 3D detection decoder. Additionally, relational graph refinement adjusts initial bounding boxes for improved localization. Experimental results indicate that MonoKey outperforms the existing monocular 3D object detection methods. The source code is available at https://anonymous.4open.science/r/MonoKey-B72B.