SEI3D: CPU-only 3D Object Tracking Fusing Sparse-flow-filtered Edge and Interior Alignment

Abstract

Monocular 3D object tracking methods are widely employed in robotic applications, however, they often struggle with low-contrast image sequences. In this paper, we introduce a novel approach to filtering redundant edges in images by leveraging sparse interior correspondences. Our method features a sparse-flow-based probability segmentation model that comprises both coarse and fine components. The coarse model evaluates the ratio of interior correspondences within a circular region centered on each pixel, while the fine model employs a binary Gaussian kernel based on the nearest interior correspondences. This probability framework facilitates the identification of control points for object edges. Additionally, we implement a robust gradient consistency-based edge connection algorithm to generate refined object edges. Utilizing these filtered edges, we formulate an edge-based energy function that accounts for object contour shape and noise uncertainty, seamlessly integrating into a multi-feature pose optimization framework. Our multi-feature fusion strategy achieves state-of-the-art performance in both public datasets and real-world applications, operating at 60 Hz using only CPU.