Predicting Unseen Object Pose with an Adaptive Depth Estimator


KIPS Transactions on Software and Data Engineering, Vol. 11, No. 12, pp. 509-516, Dec. 2022
https://doi.org/10.3745/KTSDE.2022.11.12.509,   PDF Download:
Keywords: 3D Vision, Unknown Object, 6D Pose Prediction, depth estimation, deep neural network
Abstract

Accurate pose prediction of objects in 3D space is an important visual recognition technique widely used in many applications such as scene understanding in both indoor and outdoor environments, robotic object manipulation, autonomous driving, and augmented reality. Most previous works for object pose estimation have the limitation that they require an exact 3D CAD model for each object. Unlike such previous works, this paper proposes a novel neural network model that can predict the poses of unknown objects based on only their RGB color images without the corresponding 3D CAD models. The proposed model can obtain depth maps required for unknown object pose prediction by using an adaptive depth estimator, AdaBins,. In this paper, we evaluate the usefulness and the performance of the proposed model through experiments using benchmark datasets.


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Cite this article
[IEEE Style]
S. Song and I. Kim, "Predicting Unseen Object Pose with an Adaptive Depth Estimator," KIPS Transactions on Software and Data Engineering, vol. 11, no. 12, pp. 509-516, 2022. DOI: https://doi.org/10.3745/KTSDE.2022.11.12.509.

[ACM Style]
Sungho Song and Incheol Kim. 2022. Predicting Unseen Object Pose with an Adaptive Depth Estimator. KIPS Transactions on Software and Data Engineering, 11, 12, (2022), 509-516. DOI: https://doi.org/10.3745/KTSDE.2022.11.12.509.