Pose Estimation Errors, the Ultimate Diagnosis
This paper proposes a thorough diagnosis for the problem of object detection and pose estimation. We provide a diagnostic tool to examine the impact in the performance of the different types of false positives, and the effects of the main object character
- PDF / 1,638,864 Bytes
- 17 Pages / 439.37 x 666.142 pts Page_size
- 23 Downloads / 205 Views
University of Alcal´ a, Alcal´ a de Henares, Spain [email protected], [email protected] 2 Stanford University, Stanford, USA [email protected], [email protected] 3 ESAT-PSI, IMinds, KU Leuven, Leuven, Belgium [email protected]
Abstract. This paper proposes a thorough diagnosis for the problem of object detection and pose estimation. We provide a diagnostic tool to examine the impact in the performance of the different types of false positives, and the effects of the main object characteristics. We focus our study on the PASCAL 3D+ dataset, developing a complete diagnosis of four different state-of-the-art approaches, which span from hand-crafted models, to deep learning solutions. We show that gaining a clear understanding of typical failure cases and the effects of object characteristics on the performance of the models, is fundamental in order to facilitate further progress towards more accurate solutions for this challenging task. Keywords: Object detection
1
· Pose estimation · Error diagnosis
Introduction
If there is one topic that has been obsessively drawing the attention of the computer vision research community, it has to be object detection. Object detectors are the heart of complex models able to interact with and understand our world. However, to enable a true interaction we need not only a precise localization but also an accurate pose estimation of the object. That is, just a bounding box does not help a robot to grasp an object: it needs to know a viewpoint estimate of the object to facilitate the inference of the visual affordance. Since 2006, in parallel with the enormous progress in object detection, there have been appearing different approaches which go further and propose to solve the 3D generic object localization and pose estimation problem (e.g. [1–17]). But in this ecosystem the fauna exhibits a high level of heterogeneity. Some approaches decouple the object localization and pose estimation tasks, while some do not. There is no consensus either at considering the pose estimation as a discrete or continuous problem. Different datasets, with different experimental setups and even different evaluation metrics have been proposed along the way. This paper wants to bring this situation under attention. We believe that to make progress, it is now time to consolidate the work, comparing different c Springer International Publishing AG 2016 B. Leibe et al. (Eds.): ECCV 2016, Part VII, LNCS 9911, pp. 118–134, 2016. DOI: 10.1007/978-3-319-46478-7 8
Pose Estimation Errors, the Ultimate Diagnosis
119
models proposed and drawing some general conclusions. Therefore, in the spirit of the work of Hoiem et al. [18] for the diagnosis of object detectors, we here propose a thorough diagnosis of pose estimation errors. Our work mainly provides a publicly available diagnostic tool1 to take full advantage of the results reported by state-of-the-art models in the PASCAL 3D+ dataset [19]. This can be considered our first contribution (Sect. 2). Specifically, our diagnosis first
Data Loading...
