Paper ID | 3D-2.3 | ||
Paper Title | MULTISTREAM VALIDNET: IMPROVING 6D OBJECT POSE ESTIMATION BY AUTOMATIC MULTISTREAM VALIDATION | ||
Authors | Joy Mazumder, Mohsen Zand, Michael Greenspan, Queen's University, Canada | ||
Session | 3D-2: Point Cloud Processing 2 | ||
Location | Area J | ||
Session Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation Time: | Wednesday, 22 September, 08:00 - 09:30 | ||
Presentation | Poster | ||
Topic | Three-Dimensional Image and Video Processing: Point cloud processing | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | This work presents a novel approach to improve the results of pose estimation by detecting and distinguishing between the occurrence of True and False Positive results. It achieves this by training a binary classifier on the output of an arbitrary pose estimation algorithm, and returns a binary label indicating the validity of the result. We demonstrate that our approach improves upon a state-of-the-art pose estimation result on the Siléane dataset, outperforming a variation of the alternative CullNet method by 4.15% in average class accuracy and 0.73% in overall accuracy at validation. Applying our method can also improve the pose estimation average precision results of Op-Net by 6.06% on average. |