Region-based methods have become the state-of-art solution for monocular 6-DOF object pose tracking in recent years. However, two main challenges still remain: the robustness to heterogeneous configurations (both foreground and background), and the robustness to partial occlusions. In this paper, we propose a novel region-based monocular 3D object pose tracking method to tackle these problems. Firstly, we design a new strategy to define local regions, which is simple yet efficient in constructing discriminative local color histograms. Contrary to previous methods which define multiple circular regions around the object contour, we propose to define multiple overlapped, fan-shaped regions according to polar coordinates. This local region partitioning strategy produces much less number of local regions that need to be maintained and updated, while still being temporally consistent. Secondly, we propose to detect occluded pixels using edge distance and color cues. The proposed occlusion detection strategy is seamlessly integrated into the region-based pose optimization pipeline via a pixel-wise weight function, which significantly alleviates the interferences caused by partial occlusions. We demonstrate the effectiveness of the proposed two new strategies with a careful ablation study. Furthermore, we compare the performance of our method with the most recent state-of-art region-based methods in a recently released large dataset, in which the proposed method achieves competitive results with a higher average tracking success rate. Evaluations on two real-world datasets also show that our method is capable of handling realistic tracking scenarios.

We propose a novel method for robust 6-DOF pose tracking of rigid objects from monocular images. In our method, 3D object tracking is achieved by directly aligning video frames to dynamic templates rendered from a textured 3D object model. Unlike previous methods, which usually utilize a small number of discrete templates to align with video frames, we employ an online textured model, rendering to create dynamic templates in continuous pose space according to the previously estimated object pose. In this way, a pose estimator could be easily converged to the optimal state. Besides, the rendered template also helps to detect the occlusion area by comparing it with the current frame, making our method highly robust to partial occlusions. The performance of our method is further improved by introducing a generic representation of dense images features, which we call extended dense feature fields (EDFF). Different kinds of pixel-level image features can be added to the EDFF and be optimized simultaneously in a unified Gauss-Newton optimization scheme. Attributing to dynamic templates from the textured model rendering and complementary features in EDFF, our method is able to deal with poor-textured and specular objects, as well as lighting variation and heavy occlusions. While our method is quite simple and straightforward, it achieves competitive or even superior results compared with the state of the art on challenging data sets.