The invention discloses a point cloud-oriented efficient binary neural network quantization method and a device. According to the method, an entropy-maximized aggregation function and a layer-by-layer scale recovery step are used, so that the point cloud neural network can achieve information entropy maximization, network weight and activation quantization loss minimization by minimizing a loss function in a training process, thereby reducing quantization loss and improving the performance of the binary point cloud neural network. The method is completely compatible with bitwise operation, and has the advantage of quickly deducing and quantifying the neural network. The fact shows that the method has excellent performance in various network architectures, is superior to the prior art in the aspects of memory consumption, reasoning speed and accuracy, and is particularly suitable for realizing target detection in an automatic driving scene so as to meet the real-time detection positioning requirement of an automatic driving vehicle.
Point cloud-oriented efficient binary neural network quantization method and device