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Single-view 3D reconstruction based on deep learning is a research hot spot at present. In order to discover more high-frequency details, SDF-SRN algorithm introduces positional encoding, but neural network is easy to overfit without accurate supervision, and reconstructs uneven surface. To solve the problem, this paper proposed the network model based on sparse feature. The model enabled the network that preferred to overfitting to predict high-frequency residual by residual learning. The feature extraction network extracted sparse features and the global features. Then one hypernetwork took the sparse features as input and generated prediction shallow head. This shallow head predicted low-frequency part of signed distance function. Another hypernetwork took global features as input and generated another shallow head. This shallow head predicted high-frequency residual. It fused two predictions of shallow heads into final signed distance function. Spectrum analysis shows that the design purpose of network is achieved. Compared with other smooth surface reconstruction schemes, the network can achieve smoother surface reconstruction with enough details. It overcomes the overfitting of SDF-SRN. The qualitative and quantitative comparison with other advanced single-view reconstruction approaches show the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low accuracy and residual error in small object detection on UAV aerial images, this paper proposed a new kind of multi-scale small target detection method based on enhanced lower feature. Basing on Faster RCNN ResNet-50-FPN model, the algorithm enhanced the lower feature by designing the structure of DetNet-59 feature extraction network and Flat-FPN feature fusion network, and applied soft-NMS to face the appearance of overlapping small objects. From simulation test on VOC2007 and VisDrone2019, the method is able to increase m AP by 11% compared to the base model when time consumption is no more than 2%, and it also performs better in terms of accuracy than current common algorithms. It was proved that the algorithm can effectively improve the detection accuracy of small targets while ensuring real-time performance. [ABSTRACT FROM AUTHOR]