Over the years, I have used several different 360-degree video cameras. They all behave differently, which makes it difficult to work with files from different systems. For this year’s SMC conference, we decided to compare four cameras: GoPro MAX, Insta360 X3, Garmin VIRB 360 and Ricoh Theta S.

As the table below shows, the features of the cameras vary quite a bit:

CameraFile typeProjectionCodecColorspaceResolutionFPSBitrate (kb/s)
GoPro MAX.360Equi-Angular CubemapH.265yuvj420p4096 x 26882530,002 (x2)
.LRVDual-fisheyeH.264yuvj420p1408 x 704252,499
Insta360 X3.INSVFisheye (x2)H.264yuvj420p5760 x 288029.9760,495 (x2)
.LRVDual-fisheyeH.264yuvj420p1024 x 51229.973,999
Garmin VIRB 360.MP4EquirectangularH.264yuv420p3840 x 21602580,008
.LRVEquirectangularH.264yuv420p1280 x 720255,026
Ricoh Theta S.MP4Dual-fisheyeH.264yuvj420p1920 x 108029.9715,938

The biggest practical challenge, however, is that they all use different projections:

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To support working with files from these cameras, we have added 360 support to the Musical Gestures Toolbox for Python. This allows the files to be used for regular video analysis and visualisation.

Abstract

This paper reports on a desktop investigation and a lab experiment comparing the video recording capabilities of four commercially available 360-degree cameras: GoPro MAX, Insta360 X3, Garmin VIRB 360, and Ricoh Theta S. The four cameras all use different recording formats and settings and have varying video quality and software support. This makes it difficult to conduct analyses and compare between devices. We have implemented new functions in the Musical Gestures Toolbox (MGT) for reading and merging files from the different platforms. Using the capabilities of FFmpeg, we have also made a new function for converting between different 360-degree video projections and formats. This allows (music) researchers to exploit 360-degree video recordings using regular videobased analysis pipelines.

Reference