Although the Neural Radiance Fields (NeRF) has been shown to achieve high-quality novel view synthesis, existing models still perform poorly in some scenarios, particularly unbounded scenes. These models either require excessively long training times or produce suboptimal synthesis results. Consequently, we propose SD-NeRF, which consists of a compact neural radiance field model and self-supervised depth regularization. Experimental results demonstrate that SDNeRF can shorten training time by over 20 times compared to Mip-NeRF360 without compromising reconstruction accuracy.
In recent years, with the applications of object detection increasingly extensive, the approaches based on Deep Learning have achieved state-of-the-art performance on challenging datasets. Some researchers have made demands on real-time performance while paying attention to the accuracy of the model. In addition, with the rapid development of the object detection model, the detection of small targets has attracted extensive attention. Although several evaluations of the models have been conducted, we have conducted a more detailed evaluation of the small targets real-time detection. In this work, we carried out an in-depth evaluation of the latest real-time object detection model. We evaluate three state-of-the- art models including Single Shot MultiBox Detector (SSD), You Only Look Once version 2 (YOLO v2), and You Only Look Once version 3 (YOLO v3) with related trade-off factors i.e. accuracy, execution time and resource usage. Experiments were conducted on benchmark datasets and a newly generated dataset for small object detection. All analyses and findings are then presented.
For visual tracking with UAV, the non-rigid body change of target usually results in the accumulation of errors and decline of tracking precision. In view of this problem, a target regression tracking algorithm based on convolutional neural network is proposed. Firstly, we use the Siamese convolutional neural network to extract features which used as the input of tracker based on self-adapted scale kernel correlation filters. Then, in order to cope with the cumulative errors caused by the change of target form, a target regression network is designed to refine the location. Using the refined location to extract sample and update the filter parameters of tracker can prevent tracker from being polluted. The experimental results show that the algorithm has high tracking precision as well as fast speed compared to the state-of-the-art tracking algorithms, especially with the ability to deal with the non-rigid body change of target.
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