Colourlab, a prominent research group within the Computer Science Department at NTNU – Norwegian University of Science and Technology in Gjøvik, Norway, excels in the field of colour imaging. Since its foundation in 2001, Colourlab has brought together an interdisciplinary team of experts from various scientific and technological domains. The group's research spans a broad spectrum, from fundamental colour science to complex questions such as measuring and reproducing the appearance of objects as well as understanding and modelling human perception and cognition of colour and appearance. Colourlab's work finds applications in diverse areas including cultural heritage, medical imaging, and the multimedia sector. For more information, visit our website.

This collection contains data generated by temporary as well as permanent staff members of Colourlab. The collection is managed by NTNU – Norwegian University of Science and Technology. For questions, please contact research-data@ntnu.no.
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Replication Data for: CAPTIV8 : A comprehensive large scale CAPsule endoscopy dataset for Integrated diagnosis
Jun 17, 2024
Vats, Anuja; Ahmed, Bilal; Floor, Pål Anders; Mohammed, Ahmed; Pedersen, Marius; Hovde, Øistein, 2024, "Replication Data for: CAPTIV8 : A comprehensive large scale CAPsule endoscopy dataset for Integrated diagnosis", https://doi.org/10.18710/BSXNA1, DataverseNO, V1
General description and ethics approvals: The dataset contains images and videos of wireless capsule endoscopic examinations of 10 patients focused on the large colon conducted using the PillCAM colon 2 capsule manufactured by Medtronic. In addition to images and videos it includ...
NeRF-4Scenes: A Video Dataset for Subjective Assessment of NeRF
Jun 14, 2024
Tabassum, Shaira; Amirshahi, Seyed Ali, 2024, "NeRF-4Scenes: A Video Dataset for Subjective Assessment of NeRF", https://doi.org/10.18710/LFHFJN, DataverseNO, V1
The dataset contains 36 NeRF-generated videos captured from four different indoor and outdoor environments: S1 for outdoor, S2 for auditorium, S3 for classroom, and S4 for lounge entrance. Each scene is trained using three NeRF models: Nerfacto as M1, Instant-NGP as M2, and Volin...
Replication Data for: Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations
May 10, 2024
Vats, Anuja, 2024, "Replication Data for: Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations", https://doi.org/10.18710/HSMJLL, DataverseNO, V1
The dataset comprises the pretraining and testing data for our work: Terrain-Informed Self-Supervised Learning: Enhancing Building Footprint Extraction from LiDAR Data with Limited Annotations. The pretaining data consists of images corresponding to the Digital Surface Models (DS...
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