130 citations · 297 across the 16 of their papers we have counts for
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cs.CV2017
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Benjamin Graham, Martin Engelcke, Laurens van der Maaten
Convolutional networks are the de-facto standard for analyzing spatio-temporal data such as images, videos, and 3D shapes. Whilst some of this data is naturally dense (e.g., photos…
cs.CV2017★ 53 cited
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…