Uploaded May 2018 | Updated September 2026, 1 week ago
ICRA 2018 Spotlight Video
Interactive Session Tue PM Pod O.7
Authors: Maghoumi, Mehran; LaViola, Joseph; Desingh, Karthik; Jenkins, Odest Chadwicke
Title: GemSketch: Interactive Image-Guided Geometry Extraction from Point Clouds
Abstract:
We introduce an interactive system for extracting the geometries of generalized cylinders and cuboids from single- or multiple-view point clouds. Our proposed method is intuitive and only requires the object's silhouettes to be traced by the user. Leveraging the user's perceptual understanding of what an object looks like, our proposed method is capable of extracting accurate models, even in the presence of occlusion, clutter or incomplete point cloud data, while preserving the original object's details and scale. We demonstrate the merits of our proposed method through a set of experiments on a public RGBD dataset. We extracted 16 objects from the dataset using at most two views of each object. Our extracted models represent a high degree of visual similarity to the original objects. Further, we achieved a mean normalized Hausdorff distance of 5.66% when comparing our extracted models with the dataset's ground truths.
ICRA 2018 Spotlight Video
Interactive Session Tue PM Pod O.7
Authors: Maghoumi, Mehran; LaViola, Joseph; Desingh, Karthik; Jenkins, Odest Chadwicke
Title: GemSketch: Interactive Image-Guided Geometry Extraction from Point Clouds
Abstract:
We introduce an interactive system for extracting the geometries of generalized cylinders and cuboids from single- or multiple-view point clouds. Our proposed method is intuitive and only requires the object's silhouettes to be traced by the user. Leveraging the user's perceptual understanding of what an object looks like, our proposed method is capable of extracting accurate models, even in the presence of occlusion, clutter or incomplete point cloud data, while preserving the original object's details and scale. We demonstrate the merits of our proposed method through a set of experiments on a public RGBD dataset. We extracted 16 objects from the dataset using at most two views of each object. Our extracted models represent a high degree of visual similarity to the original objects. Further, we achieved a mean normalized Hausdorff distance of 5.66% when comparing our extracted models with the dataset's ground truths.
