The paper presents an unsupervised 3D shape co-segmentation method called DAE-Net (Deforming Auto-Encoder). The key idea is to learn a set of deformable part templates that can be affine-transformed and further deformed to reconstruct each shape in the collection.
The network architecture consists of an N-branch autoencoder, where each branch represents a part template. The CNN encoder takes a voxelized shape as input and produces affine transformation matrices, part latent codes, and part existence scores to select and transform the required part templates. The decoder then deforms the transformed part templates using per-part deformation networks to refine the part details.
The training scheme includes a shape reconstruction loss, a deformation constraint loss, and a sparsity loss to encourage compact and consistent segmentation. The authors also propose a training scheme to effectively overcome local minima encountered during training.
Extensive experiments on the ShapeNet Part dataset, DFAUST, and an animal subset of Objaverse show that DAE-Net outperforms prior unsupervised shape co-segmentation methods, producing fine-grained, meaningful, and consistent part segmentation across diverse shapes. The authors also demonstrate shape clustering and a controllable shape detailization application enabled by the segmentation results.
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by Zhiqin Chen,... a las arxiv.org 04-29-2024
https://arxiv.org/pdf/2311.13125.pdfConsultas más profundas