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Title A Convolutional Architecture for 3D Model Embedding using Image Views
Authors Arniel Labrada, Benjamin Bustos, Iván Sipirán
Publication date March 2024
Abstract During the last years, many advances have been made in
tasks like
3D model retrieval, 3D model classification, and 3D model segmentation. The
typical 3D representations such as point clouds, voxels, and polygon meshes
are mostly suitable for rendering purposes, while their use for cognitive
processes (retrieval, classification, segmentation) is limited due to their
high redundancy and complexity. We propose a deep learning architecture to
handle 3D models represented as sets of image views as input. Our proposed
architecture combines other standard architectures, like Convolutional
Neural Networks and autoencoders, for computing 3D model embeddings using
sets of image views extracted from the 3D models, avoiding the common view
pooling layer approach used in these cases. Our goal is to represent a 3D
model as a vector with enough information so it can substitute the 3D model
for high-level tasks. Since this vector is a learned representation which
tries to capture the relevant information of a 3D model, we show that the
embedding representation conveys semantic information that helps to deal
with the similarity assessment of 3D objects. We compare our proposed
embedding technique with state-of-the-art techniques for 3D Model Retrieval
using the ShapeNet and ModelNet datasets. We show that the embeddings
obtained with our proposed architecture allow us to obtain a high
effectiveness score in both normalized and perturbed versions of the
ShapeNet dataset while improving the training and inference times compared
to the standard state-of-the-art techniques.
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Pages 1601-1615
Volume 40
Journal name The Visual Computer
Publisher Springer (New York, NY, USA)