QaQ: Robust 6D Pose Estimation via Quality-Assessed RGB-D Fusion

Théo Petitjean, Zongwei Wu, Olivier Laligant, Cédric Demonceaux

Abstract: RGB-D 6D pose estimation has recently drawn great research attention thanks to the complementary depth information. Whereas, the depth and the color image are often noisy in real industrial scenarios. Therefore, it becomes challenging for many existing methods that fuse equally RGB and depth features. In this paper, we present a novel fusion design to adaptively merge RGB-D cues. Specifically, we created a Quality-assessment block that estimates the global quality of the input modalities. This quality represented as an α parameter is then used to reinforce the fusion. We have thus found a simple and effective way to improve the robustness to low-quality inputs in terms of Depth and RGB. Extensive experiments on 6D pose estimation demonstrate the efficiency of our method, especially when noise is present in the input.