Artificial Intelligence

Forest wildfires are one of the most catastrophic natural disasters, which poses a severe threat to both the ecosystem and human life. Therefore, it is imperative to implement technology to prevent and control forest wildfires. The combination of unmanned aerial vehicles (UAVs) and object detection algorithms provides a quick and accurate method to monitor large-scale forest areas.

As with most AI methods, a 3D deep neural network needs to be trained to properly interpret its input data. More specifically, training a network for monocular 3D point cloud reconstruction requires a large set of recognized high-quality data which can be challenging to obtain. Hence, this dataset contains the image of a known object alongside its corresponding 3D point cloud representation. To collect a large number of categorized 3D objects, we use the ShapeNetCore (https://shapenet.org) dataset.

Large vision-language models (LVLMs) suffer from hallucination, generating responses that apparently contradict to the image content occasionally. The key problem lies in its weak ability to comprehend detailed content in multi-modal contexts, which can be mainly attributed its training data. The vision instruction dataset primarily focuses on global description that are highly relevant to the image, with few samples containing image details.

This dataset is a valuable resource for those working on daylight illumination-related projects. It includes a comprehensive collection of images captured under various daylight conditions. These images can be used for tasks such as illumination estimation, scene relighting, and object insertion. The dataset features scenes illuminated by different intensities and angles of daylight, providing a rich set of examples for realistic daylight simulation.