How to aggregate multi-view representations of a 3D object into an
informative and discriminative one remains a key challenge for multi-view 3D
object retrieval. Existing methods either use view-wise pooling strategies
which neglect the spatial information across different views or employ
recurrent neural networks which may face the efficiency problem. To address
these issues, we propose an effective and efficient framework called View
N-gram Network (VNN). Inspired by n-gram models in natural language processing,
VNN divides the view sequence into a set of visual n-grams, which involve
overlapping consecutive view sub-sequences. By doing so, spatial information
across multiple views is captured, which helps to learn a discriminative global
embedding for each 3D object. Experiments on 3D shape retrieval benchmarks,
including ModelNet10, ModelNet40 and ShapeNetCore55 datasets, demonstrate the
superiority of our proposed method.

本文介绍了一种名为 View N-gram Network (VNN) 的高效框架，通过将视图序列划分为一组视觉 n-gram，VNN 可以捕获多个视图间的空间信息，从而帮助学习每个 3D 对象的判别性全局嵌入。在诸如 ModelNet10、ModelNet40 和 ShapeNetCore55 等 3D 形状检索基准测试中，我们的方法表现出优越性。