3D Motion Perception of Binocular Vision Target with PID-CNN

• URL: http://arxiv.org/abs/2511.20332v2
• Date: Mon, 01 Dec 2025 07:41:45 GMT
• Title: 3D Motion Perception of Binocular Vision Target with PID-CNN
• Authors: Jiazhao Shi, Pan Pan, Haotian Shi,
• Abstract summary: This article trained a network for perceiving three-dimensional coordinate errors, velocity, and acceleration, and has a basic perception capability.<n>Designed a relatively small convolutional neural network, with a total of 17 layers and 413 thousand parameters.
• Score: 10.329773750968926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This article trained a network for perceiving three-dimensional motion information of binocular vision target, which can provide real-time three-dimensional coordinate, velocity, and acceleration, and has a basic spatiotemporal perception capability. Understood the ability of neural networks to fit nonlinear problems from the perspective of PID. Considered a single-layer neural network as using a second-order difference equation and a nonlinearity to describe a local problem. Multilayer networks gradually transform the raw representation to the desired representation through multiple such combinations. Analysed some reference principles for designing neural networks. Designed a relatively small PID convolutional neural network, with a total of 17 layers and 413 thousand parameters. Implemented a simple but practical feature reuse method by concatenation and pooling. The network was trained and tested using the simulated randomly moving ball datasets, and the experimental results showed that the prediction accuracy was close to the upper limit that the input image resolution can represent. Analysed the experimental results and errors, as well as the existing shortcomings and possible directions for improvement. Finally, discussed the advantages of high-dimensional convolution in improving computational efficiency and feature space utilization. As well as the potential advantages of using PID information to implement memory and attention mechanisms.

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