The PointDenoisingBenchmark dataset features 28 different shapes, split into 18 training shapes and 10 test shapes. * PointDenoisingBenchmark for outliers removal: contains noisy point clouds with different levels of gaussian noise and the corresponding clean ground truths. * PointDenoisingBenchmark for denoising: contains noisy point clouds with different levels of noise and density of outliers and the corresponding clean ground truths. Source: PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
The Raw Natural Image Noise Dataset (RawNIND) is a diverse collection of paired raw images designed to support the development of denoising models that generalize across sensors, image development workflows, and styles. RawNIND contains 2831 raw images (2279 noisy and 562 clean) split across 310 scenes and 11 cameras.
We propose a new light field image database called “PINet” inheriting the hierarchical structure from WordNet. It consists of 7549 LIs captured by Lytro Illum, which is much larger than the existing databases. The images are manually annotated to 178 categories according to WordNet, such as cat, camel, bottle, fans, etc. The registered depth maps are also provided. Each image is generated by processing the raw LI from the camera by Light Field Toolbox v0.4 for demosaicing and devignetting. PINet is the largest light field image dataset. More details can be found in https://github.com/VincentChandelier/SADN