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Found 4 papers, 4 papers with code
Discrete Point Flow Networks for Efficient Point Cloud Generation
Generative models have proven effective at modeling 3D shapes and their statistical variations.
Probabilistic Reconstruction Networks for 3D Shape Inference from a Single Image
We study end-to-end learning strategies for 3D shape inference from images, in particular from a single image.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds.
Ranked #55 on
3D Part Segmentation
on ShapeNet-Part
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