3DPoseLite: A Compact 3D Pose Estimation Using Node Embeddings

• Published in: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) pp. 1877 - 1886
• Main Authors: Dani, Meghal, Narain, Karan, Hebbalaguppe, Ramya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2021
• Subjects: Computational modeling; Computer vision; Conferences; Neural networks; Pose estimation; Solid modeling; Three-dimensional displays
• ISSN: 2642-9381
• DOI: 10.1109/WACV48630.2021.00192

Efficient pose estimation finds utility in Augmented Reality (AR) and other computer vision applications such as autonomous navigation and robotics, to name a few. A compact and accurate pose estimation methodology is of paramount importance for on-device inference in such applications. Our proposed solution 3DPoseLite, estimates pose of generic objects by utilizing a compact node embedding representation, unlike computationally expensive multi-view and point-cloud representations. The neural network outputs a 3D pose, taking RGB image and its corresponding graph (obtained by skeletonizing the 3D meshes [31]) as inputs. Our approach utilizes node2vec framework to learn low-dimensional representations for nodes in a graph by optimizing a neighborhood preserving objective. We achieve a space and time reduction by a factor of 11 × and 3 × respectively, with respect to the state-of-the-art approach, Pose-FromShape [50], on benchmark Pascal3D dataset [48]. We also test the performance of our model on unseen data using Pix3D dataset.