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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11 to 20 of 21 Results
ZIP Archive - 3.3 MB - MD5: 40d912cdb695f742da209889f53ff74e
Colorchecker and checkerboard images/video for camera calibration
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...
ZIP Archive - 1.4 GB - MD5: 83cc7e343559ad1b1f1542d8ef9993aa
Adobe PDF - 4.7 MB - MD5: 7dc0c99d121cd508561a743355a25f86
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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