Shadow Detection

SBU-Kinect-Interaction dataset version 2.0 comprises of RGB-D video sequences of humans performing interaction activities that are recording using the Microsoft Kinect sensor. This dataset was originally recorded for a class project, and it must be used only for the purposes of research. If you use this dataset in your work, please cite the following paper. Kiwon Yun, Jean Honorio, Debaleena Chattopadhyay, Tamara L. Berg, and Dimitris Samaras, The 2nd International Workshop on Human Activity Understanding from 3D Data at Conference on Computer Vision and Pattern Recognition (HAU3D-CVPRW), CVPR 2012 SBU-Refine: SBU-Refine relabels the test set manually and refines the noise labels in training set by algorithm. H. Yang, T. Wang, X. Hu, and C.-W. Fu, “SILT: Shadow-aware iterative label tuning for learning to detect shadows from noisy labels,” in ICCV, 2023, pp. 12 687–12 698.

A new dataset called SOBA, named after Shadow-OBject Association, with 3,623 pairs of shadow and object instances in 1,000 photos, each with individual labeled masks. Source: Instance Shadow Detection

Collects shadow images for multiple scenarios and compiled a new dataset of 10,500 shadow images, each with labeled ground-truth mask, for supporting shadow detection in the complex world. The dataset covers a rich variety of scene categories, with diverse shadow sizes, locations, contrasts, and types. Source: Revisiting Shadow Detection: A New Benchmark Dataset for Complex World

A significant challenge in removing shadows from indoor scenes is obtaining shadow-free images. To overcome this challenge, we propose a novel rendering pipeline for generating shadowed and shadow-free images under direct and indirect illumination, and create a comprehensive synthetic dataset that contains over 30,000 image pairs, covering various object types and lighting conditions. We implemented a direct/indirect shadow and shadow-free rendering pipeline using Blender Cycles engine, with the help of Open Shading Language (OSL). The resulting collection of shadow and shadow-free images is referred to as the “INS dataset”. The dataset includes 30,000 training and 2,000 testing images, all with a resolution of 512 × 512. The training and testing images are generated from distinct scenes with different objects and materials.