HF-Net is a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization. 6-DoF visual localization method is accurate, scalable, and efficient, using HF-Net, a monolithic deep neural network for descriptor extraction. The proposed solution achieves state-of-the-art accuracy on several large-scale public benchmarks while running in real-time. For more details about HF-Net, refer to https://arxiv.org/abs/1812.03506.
The following image shows the result of HFNet.
Figure 1. HFNet Example
The following table lists the HFNet models supported by the Vitis AI Library.