We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings.
Melzi, S., Spezialetti, R., Tombari, F., Bronstein, M., Di Stefano, L., Rodolà, E. (2019). GFrames: gradient-based local reference frame for 3d shape matching. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp.4624-4633). IEEE COMPUTER SOC [10.1109/CVPR.2019.00476].
GFrames: gradient-based local reference frame for 3d shape matching
Melzi, S;
2019
Abstract
We introduce GFrames, a novel local reference frame (LRF) construction for 3D meshes and point clouds. GFrames are based on the computation of the intrinsic gradient of a scalar field defined on top of the input shape. The resulting tangent vector field defines a repeatable tangent direction of the local frame at each point; importantly, it directly inherits the properties and invariance classes of the underlying scalar function, making it remarkably robust under strong sampling artifacts, vertex noise, as well as non-rigid deformations. Existing local descriptors can directly benefit from our repeatable frames, as we showcase in a selection of 3D vision and shape analysis applications where we demonstrate state-of-the-art performance in a variety of challenging settings.File | Dimensione | Formato | |
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