Inertial sensors improve scale accuracy in 360 degree camera mapping
Metric Scale for Insta360 Structure-from-Motion from the Camera's Embedded IMU
Computer Vision and Pattern Recognition
Summary
3D models made from 360-degree camera video usually miss exact size information. The authors show that the tiny motion sensors inside Insta360 cameras can fix this by figuring out the correct scale without needing extra smartphone data. They tested their method against laser scanning and smartphone references, finding the sensors give more accurate size estimates. This means better real-world measurements from just the camera itself.
What this means in practice
- •For surveying teams: Create accurately scaled 3D maps from Insta360 videos using the camera’s internal sensors without extra smartphone hardware.
- •For augmented reality developers: Improve metric scale estimation in 360-degree capture apps by leveraging embedded inertial sensors instead of external smartphone tracking.
Authors
Davide Cuttini, Diego Di Tommaso, Alessio Antoniacomi, Luca Simonetti
Abstract
Structure-from-motion (SfM) from the dual-fisheye video of a consumer 360-degree camera recovers geometry only up to a similarity transform; in the pipeline considered here, metric scale is obtained from smartphone ARKit or ARCore poses captured alongside the camera. This paper evaluates a self-contained alternative: the IMU embedded in the Insta360 X5 and X6, recorded at 1 kHz. The sensor is characterised (noise density, camera-IMU synchronisation, accelerometer scale factor, bias stability), the camera-IMU rotation and frame timing are estimated from the data, and the scale is recovered by a linear visual-inertial alignment over the SfM poses of a whole survey. The standard formulation, with gravity and accelerometer bias shared across the survey, overestimates the scale by 3.4% on average; estimating each contiguous segment independently and averaging removes the bias. On nine surveys with a co-mounted LiDAR scanner the inertial scale deviates from the LiDAR by 0.74% in mean absolute error, against 1.94% for the smartphone scale; on printed fiducial markers both agree within the 1% uncertainty of the target. On the reference trajectories of thirty sequences of a public benchmark, brought to scales of 1/40, 1 and 40, the estimator recovers the metric scale with a mean error of -0.07% and a standard deviation of 1.28%. On fifty production surveys where the smartphone is the only reference, the two scales agree to a standard deviation of 2.6%, the inertial scale being 0.9% lower on average; the difference does not depend on window construction, grows with the extent of the smartphone anchor, and cannot be attributed to either reference. An ablation over window construction and a comparison with monocular metric-depth networks complete the study.