We propose a geometry-guided neural network architecture for robust and detail-preserving surface normal estimation for unstructured point clouds.
Nov 8, 2024 · A new two-step normal estimation method. Integrate geometric priors into deep learning framework. Replace multi-scale architecture by multi-scale geometric ...
Abstract: We propose a geometry-guided neural network architecture for robust and detail-preserving surface normal estimation for unstructured point clouds.
We propose a geometry-guided neural network architecture for robust and detail-preserving surface normal estimation for unstructured point clouds.
Dec 9, 2024 · We propose a geometry-guided neural network architecture for robust and detail-preserving surface normal estimation for unstructured point ...
This work presents an accurate and robust method for estimating normals from point clouds using a new metric termed Chamfer Normal Distance and devise an ...
Nov 8, 2024 · Abstract: Highlights•A new two-step normal estimation method.•Integrate geometric priors into deep learning framework.•Replace multi-scale ...
In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth ...
Abstract. We propose a deep convolutional neural network (CNN) to estimate surface normal from a single color image accompanied with.
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Jan 11, 2024 · We propose the use of a Transformer to accurately predict normals from point clouds with noise and density variations.