Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection
This work addresses the need for more accurate plane segmentation in logistics applications, where cuboid-shaped objects are common, but it is incremental as it builds on existing segmentation techniques.
The paper tackles the problem of inaccurate fine-grained details in plane segmentation masks from single RGB images by proposing a post-processing algorithm that aligns segmented planes with image edges, specifically for cuboid-shaped objects, resulting in consistent improvements over state-of-the-art methods as reported on their own dataset.
Recent advances in the area of plane segmentation from single RGB images show strong accuracy improvements and now allow a reliable segmentation of indoor scenes into planes. Nonetheless, fine-grained details of these segmentation masks are still lacking accuracy, thus restricting the usability of such techniques on a larger scale in numerous applications, such as inpainting for Augmented Reality use cases. We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image. This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects. Our approach is motivated by logistics, where this assumption is valid and refined planes can be used to perform robust object detection without the need for supervised learning. Results for two baselines and our approach are reported on our own dataset, which we made publicly available. The results show a consistent improvement over the state-of-the-art. The influence of the prior segmentation and the edge detection is investigated and finally, areas for future research are proposed.