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DPA-Net: Structured 3D Abstraction from Sparse Views via Differentiable Primitive Assembly
We present a differentiable rendering framework to learn structured 3D abstractions in the form of primitive assemblies from sparse RGB images capturing a 3D object.
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling
We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the small and intricate parts.
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