BAE-NET: Branched Autoencoder for Shape Co-Segmentation

03/27/2019
by   Zhiqin Chen, et al.
10

We treat shape co-segmentation as a representation learning problem and introduce BAE-NET, a branched autoencoder network, for the task. The unsupervised BAE-NET is trained with all shapes in an input collection using a shape reconstruction loss, without ground-truth segmentations. Specifically, the network takes an input shape and encodes it using a convolutional neural network, whereas the decoder concatenates the resulting feature code with a point coordinate and outputs a value indicating whether the point is inside/outside the shape. Importantly, the decoder is branched: each branch learns a compact representation for one commonly recurring part of the shape collection, e.g., airplane wings. By complementing the shape reconstruction loss with a label loss, BAE-NET is easily tuned for one-shot learning. We show unsupervised, weakly supervised, and one-shot learning results by BAE-NET, demonstrating that using only a couple of exemplars, our network can generally outperform state-of-the-art supervised methods trained on hundreds of segmented shapes.

READ FULL TEXT

page 5

page 6

research
12/06/2018

Learning Implicit Fields for Generative Shape Modeling

We advocate the use of implicit fields for learning generative models of...
research
03/25/2019

CoSegNet: Deep Co-Segmentation of 3D Shapes with Group Consistency Loss

We introduce CoSegNet, a deep neural network architecture for co-segment...
research
10/08/2021

Meta-Learning 3D Shape Segmentation Functions

Learning robust 3D shape segmentation functions with deep neural network...
research
12/19/2017

Y-net: 3D intracranial artery segmentation using a convolutional autoencoder

Automated segmentation of intracranial arteries on magnetic resonance an...
research
10/16/2020

Training Data Generating Networks: Linking 3D Shapes and Few-Shot Classification

We propose a novel 3d shape representation for 3d shape reconstruction f...
research
05/10/2020

A Simple and Scalable Shape Representation for 3D Reconstruction

Deep learning applied to the reconstruction of 3D shapes has seen growin...
research
09/14/2018

SCORES: Shape Composition with Recursive Substructure Priors

We introduce SCORES, a recursive neural network for shape composition. O...

Please sign up or login with your details

Forgot password? Click here to reset