4 papers
Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Parts
Yutaka Yoshinaga, Naoya Chiba, Koichi Hashimoto
Recent advances in large pre-trained models have led to remarkable progress in instance segmentation on general images. However, industrial scenarios remain challenging. Instance d…
Hierarchical Image-Guided 3D Point Cloud Segmentation in Industrial Scenes via Multi-View Bayesian Fusion
Yu Zhu, Naoya Chiba, Koichi Hashimoto
Reliable 3D segmentation is critical for understanding complex scenes with dense layouts and multi-scale objects, as commonly seen in industrial environments. In such scenarios, he…
NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields
Floris Erich, Naoya Chiba, Yusuke Yoshiyasu +3
We present NeuralLabeling, a labeling approach and toolset for annotating 3D scenes using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bou…
3D Point Cloud Registration with Learning-based Matching Algorithm
Rintaro Yanagi, Atsushi Hashimoto, Shusaku Sone +3
We present a novel differential matching algorithm for 3D point cloud registration. Instead of only optimizing the feature extractor for a matching algorithm, we propose a learning…