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

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. However, as previously observed [26], visual ambiguities can also happen depending on the viewpoint or the presence of occluding objects, when disambiguating parts become hidden. The visual ambiguities are therefore actually different across images. 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. Given this improved ground truth, we re-evaluate the state-of-the-art methods and show this greatly modify the ranking of these methods. Our annotations also allow us to benchmark recent methods able to estimate a pose distribution on real images for the first time. We will make our annotations for the T-LESS dataset and our code publicly available.

Challenges in automatic and selective plant-clearing

With the advent of multispectral imagery and AI, there have been numerous works on automatic plant segmentation for purposes such as counting, picking, health monitoring, localized pesticide delivery, etc. In this paper, we tackle the related problem of automatic and selective plant-clearing in a sustainable forestry context, where an autonomous machine has to detect and avoid specific plants while clearing any weeds which may compete with the species being cultivated. Such an autonomous system requires a high level of robustness to weather conditions, plant variability, terrain and weeds while remaining cheap and easy to maintain. We notably discuss the lack of robustness of spectral imagery, investigate the impact of the reference database's size and discuss issues specific to AI systems operating in uncontrolled environments.