标题： 分层抽象使深度学习模型能够实现类人3D物体识别 显示英文标题

标题： Hierarchical Abstraction Enables Human-Like 3D Object Recognition in Deep Learning Models

摘要： 人类和深度学习模型都可以从用稀疏视觉信息描绘的3D形状中识别物体，例如从3D物体表面随机采样的点集（称为点云）。 尽管深度学习模型在从3D形状中识别物体方面达到类似人类的性能，但尚不清楚这些模型是否发展出与人类视觉用于物体识别的3D形状表示相似的表示。 我们假设，使用3D形状进行训练可以使模型形成3D形状中局部几何结构的表示。 然而，它们对全局3D物体形状的表示可能是有限的。 我们系统地操纵点密度和物体方向（实验1），以及局部几何结构（实验2）进行了两项人类实验。 人类在所有实验条件下都表现良好。 我们比较了两种类型的深度学习模型，一种基于卷积神经网络（DGCNN），另一种基于视觉变压器（点变压器），并与人类表现进行比较。 我们发现，点变压器模型比基于卷积的模型更能解释人类表现。 这种优势主要来自于点变压器模型中的机制，该机制支持3D形状的层次抽象。 显示英文摘要

摘要： Both humans and deep learning models can recognize objects from 3D shapes depicted with sparse visual information, such as a set of points randomly sampled from the surfaces of 3D objects (termed a point cloud). Although deep learning models achieve human-like performance in recognizing objects from 3D shapes, it remains unclear whether these models develop 3D shape representations similar to those used by human vision for object recognition. We hypothesize that training with 3D shapes enables models to form representations of local geometric structures in 3D shapes. However, their representations of global 3D object shapes may be limited. We conducted two human experiments systematically manipulating point density and object orientation (Experiment 1), and local geometric structure (Experiment 2). Humans consistently performed well across all experimental conditions. We compared two types of deep learning models, one based on a convolutional neural network (DGCNN) and the other on visual transformers (point transformer), with human performance. We found that the point transformer model provided a better account of human performance than the convolution-based model. The advantage mainly results from the mechanism in the point transformer model that supports hierarchical abstraction of 3D shapes.