Shadow-related Application

Posted on | In

Application

  • Shadow detection: [1]

  • Object-shadow pair detection/matting: [2] [10] [11]

  • Shadow removal: [3] [4] [5] [6]

  • Shadow generation: [7] [8]

  • Remove occluder and its associated shadow [9]

Dataset

Shadow Generation

  1. Shadow-AR (rendered) paper
  2. RGB-AO-depth (rendered) paper
  3. Composition datasets: WILDTRACK, Penn-Fudan, UA-DETRAC, Cityscapes, ShapeNet paper
  4. Soft shadow dataset (rendered) paper
  5. ShadowGAN (rendered, 12,400 rendered images, 9265 objects, 110 textures for rendering the plane, up to four objects in each scene) paper
  6. SID (single object, 25, 000 images, 12, 500 3D objects, 50 homogeneous color and 200 variable set of textured patterns) paper
  7. SID2 (45,000 images, similar to SID, more than one object in each scene) paper
  8. SHAD3S paper
  9. DESOBA paper

Shadow Removal/Detection

  1. ISTD/ ISTD+ (1870 0 triplets of shadow, shadow mask and shadow-free images) paper
  2. USR(unpaired, 2,445 shadow images, 1,770 shadow-free) paper
  3. SRD/ SRD+ (3088 pairs, paired shadow and shadow-free, without the ground-truth shadow mask) paper
  4. LRSS (37 image pairs, soft shadow) paper
  5. UIUC (76 pairs, paired shadow/shadow-free) paper
  6. GTAV (5723 pairs, 5110 daylight scenes, occlude objects inside camera) paper
  7. SynShadow (based on USR, occlude objects outside camera, shadow/shadow-free/matte image triplets synthesized from rendered 10,000 matte images and about 1,800 background images) paper
  8. UCF (245 pairs, shadow/shadow mask, only for detection)
  9. SBU (4727 pairs, shadow/shadow mask, only for detection)
  10. CUHK-Shadow (10,500 pairs, shadow/shadow mask, only for detection) paper
  11. SOBA (1013 images) paper
  12. AISD (514 pairs, shadow/shadow mask, only for detection, areial images) paper
  13. video shadow removal dataset (8 videos, shadow/shadow mask/shadow free) paper
  14. CMU dataset(135 pairs, shadow/shadow boundaries) paper
  15. ViSha (120 videos with 11685 frames) paper
  16. VISAD (82 videos, half-annotated) paper

References

  1. Zhu, Lei, et al. “Bidirectional feature pyramid network with recurrent attention residual modules for shadow detection.” Proceedings of the European Conference on Computer Vision (ECCV). 2018.

  2. Wang, Tianyu, et al. “Instance shadow detection.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

  3. Hu, Xiaowei, et al. “Mask-ShadowGAN: Learning to remove shadows from unpaired data.” Proceedings of the IEEE International Conference on Computer Vision. 2019.

  4. Le, Hieu, and Dimitris Samaras. “Shadow removal via shadow image decomposition.” Proceedings of the IEEE International Conference on Computer Vision. 2019.

  5. Xiaodong, Cun, Pun Chi-Man, and Shi Cheng. “Towards Ghost-free Shadow Removal via Dual Hierarchical Aggregation Network and Shadow Matting GAN.” arXiv preprint arXiv:1911.08718 (2019).

  6. Le, Hieu, and Dimitris Samaras. “From Shadow Segmentation to Shadow Removal.” European Conference on Computer Vision. Springer, Cham, 2020.

  7. Liu, Daquan, et al. “ARShadowGAN: Shadow Generative Adversarial Network for Augmented Reality in Single Light Scenes.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020.

  8. Zhan, Fangneng, et al. “Adversarial Image Composition with Auxiliary Illumination.” Proceedings of the Asian Conference on Computer Vision. 2020.

  9. Zhang, Edward, et al. “No Shadow Left Behind: Removing Objects and their Shadows using Approximate Lighting and Geometry.” CVPR, 2021.

  10. Wang, Tianyu, et al. “Single-stage instance shadow detection with bidirectional relation learning.” CVPR, 2021.

  11. Lu, Erika, et al. “Omnimatte: Associating objects and their effects in video.” CVPR, 2021.