BOP-Distrib: Revisiting 6D Pose Estimation Benchmark for Better Evaluation under Visual Ambiguities

• URL: http://arxiv.org/abs/2408.17297v2
• Date: Fri, 15 Nov 2024 10:35:19 GMT
• Title: BOP-Distrib: Revisiting 6D Pose Estimation Benchmark for Better Evaluation under Visual Ambiguities
• Authors: Boris Meden, Asma Brazi, Fabrice Mayran de Chamisso, Steve Bourgeois,
• Abstract summary: 6D pose estimation aims at determining the pose of the object that best explains the camera observation. Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visual ambiguities as only related to global object symmetries. We propose an automatic method to re-annotate those datasets with a 6D pose distribution specific to each image, taking into account the visibility of the object surface in the image to correctly determine the visual ambiguities.
• Score: 0.7499722271664147
• License:
• Abstract: 6D pose estimation aims at determining the pose of the object that best explains the camera observation. The unique solution for a non-symmetrical object can turn into a multi-modal pose distribution for a symmetrical object or when occlusions of symmetry-breaking elements happen, depending on the viewpoint. Currently, 6D pose estimation methods are benchmarked on datasets that consider, for their ground truth annotations, visual ambiguities as only related to global object symmetries, whereas they should be defined per-image to account for the camera viewpoint. We thus first propose an automatic method to re-annotate those datasets with a 6D pose distribution specific to each image, taking into account the visibility of the object surface in the image to correctly determine the visual ambiguities. Second, given this improved ground truth, we re-evaluate the state-of-the-art single pose methods and show that this greatly modifies the ranking of these methods. Third, as some recent works focus on estimating the complete set of solutions, we derive a precision/recall formulation to evaluate them against our image-wise distribution ground truth, making it the first benchmark for pose distribution methods on real images. We will make our annotations for the T-LESS dataset and our code publicly available.

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