Trace-Level Invisible Enhanced Network for 6D Pose Estimation

Hanbo Sang, Zelin Ni, Huanyu He, Xuesong Gao, Qihao Sun, Lei Qu, Sihai Zhang, Wenwen Wang, Supavadee Aramvith, Weiyao Lin

Abstract: Estimating 6D pose of the object from a single image is es-sential for robotic manipulation. Many recent learning-based methods directly regress the pose from 2D-3D points corre-spondence. The problem is that, these methods only make use of visible information from the single-view image, resulting ambiguity for the network to solve pose from the limited cor-responding pairs. To overcome this problem, this paper intro-duces INVNet, integrating invisible information into the visi-ble 2D-3D correspondence to model geometry features of the 3D object. Instead of directly reconstruct the coordinate of in-visible points, we propose Trace-level Geometry Path, which estimates the trace-level depth of the object model for each image pixel. Specifically, our INVNet generates dense visible correspondence as well as Trace-level Geometry Path map, then learn to solve 6D pose from them. Meanwhile, each cam-era ray along with Trace-level Geometry Path is transformed to the object space by the predicted pose to compute invisi-ble correspondence loss from visible one, back to enhance its learning. Extensive experiments show that our approach out-performs state-of-the-art methods on the benchmark LM and LM-O datasets.