DOI: 10.5281/zenodo.10221426
title: HauntedLamp

description: A Multi-camera Dataset: HauntedLamp by LISA ULB and ETRI
The HauntedLamp sequence is provided by Laurie Van Bogaert², Andrew Karam*, Armand Losfeld², Gun Bang³, Jinho Lee³, Gauthier Lafruit², Mehrdad Teratani². 
*student at EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium.
²members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium.
³members of Electronics and Telecommunications Research Institute, ETRI, Korea
Production
Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.
Terms of use
Any kind of publication or report using this dataset should refer to the following reference:Laurie Van Bogaert, Andrew Karam, Armand Losfeld, Gun Bang, Jinho Lee, Mehrdad Teratani, Gauthier Lafruit. "[INVR] Proposition of a Multi-camera Dataset: HauntedLamp", ISO/IEC JTC 1/SC 29/WG 4, m64774, October 2023, Hannover, Germany.
Content
The HauntedLamp dataset has been captured using 12 (3x4) synchronized Black-Magic cameras on a structure that is an arc of a circle as seen in Figure 1.  The part of the circle arc occupied by the camera is of length 80cm (width) and 50 cm (height) with a rayon of length 2 meters. The acquisition is done using a DELTA-12G-e-h 2i1c acquisition card, an ESE ES 208A clock amplifier, and 12 Blackmagic Micro Studio 4K cameras. The scene represents an office environment with a person, a lamp, and moving objects. In the sequence, the person is cleaning a blackboard, stops and sits down, and then takes a yellow lamp to protect himself from the haunted lamp approaching him. The dataset has been acquired and calibrated in the scope of a master thesis at the Polytechnical School of Brussels, Université Libre de Bruxelles, Belgium [Karam2023].
raw.zip
`Raw.zip` contains twelve synchronized raw views with 456 frames acquired at a speed of 30 frames per second (YUV422-8bits-packed).
corrected.zip
`Corrected.zip` contains twelve undistorted and color-corrected videos of 300 frames (format: YUV420-8bits/yuv420p, 1920x1080 pixels). In addition, the folder contains the `cams_MultiCam_1920x1080.json` file with the camera parameters (intrinsic and extrinsic). This file uses the OMAF coordinates system (Camera position: X: forwards, Y: left, Z: up, Rotation: yaw, pitch, roll).
ngpTest.zip
The `ngpTest.zip` folder contains an instant-ngp [NGP] model (base2_35000.ingp) trained for 35000 steps with the frame 276 of the camera array, as well as the images and `transforms.json` file used. Inside this folder, the `screenshotTransforms/screen_transforms_p_30.json` file contains a path with 30 intermediate positions between each of the 12 input cameras. The video of this path is present in the `PreviewVideos.zip` folder.
previewVideos.zip
This folder contains two mp4 videos. The first video is the sequence from camera 6 (`1_cam6.mp4`). The second video (`2_instant_ngp_camera_path_f276.mp4`) navigates in between the cameras and is synthesized with instant-ngp (the configuration file is the `screenshotTransforms/screen_transforms_p_30.json` file in the `ngpTest.zip` folder).
License
CC BY-NC-SA
References

[Karam2023] Andrew Karam. (2023) "Room-size Multicamera System Setup for VR Applications" (Master's thesis, Université libre de Bruxelles, Brussels, Belgium)
[NGP] Müller, T., Evans, A., Schied, C., & Keller, A. (2022). Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG), 41(4), 1-15.
description: Laurie Van Bogaert is funded by the Walloon Regio under a FRIA grant, of the Fond de la Recherche Scientifique (FNRS), Belgium. This work is supported in part by the FER 2021 project (n◦ 1060H000066-FAISAN); in part by the Emile DEFAY 2021 project (n◦ 4R00H000236); in part by the FER 2023 project (n◦ 1060H000075), Belgium; and in part by Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2017-0-00072, Development of Audio/Video Coding and Light Field Media Fundamental Technologies for Ultra Realistic Tera media).