Simple yet effective 3D ego-pose lift-up based on vector and distance for a mounted omnidirectional camera

Abstract

Following the advances in convolutional neural networks and synthetic data generation, 3D egocentric body pose estimations from a mounted fisheye camera have been developed. Previous works estimated 3D joint positions from raw image pixels and intermediate supervision during the process. The mounted fisheye camera captures notably different images that are affected by the optical properties of the lens, angle of views, and setup positions. Therefore, 3D ego-pose estimation from a mounted fisheye camera must be trained for each set of camera optics and setup. We propose a 3D ego-pose estimation from a single mounted omnidirectional camera that captures the entire circumference by back-to-back dual fisheye cameras. The omnidirectional camera can capture the user’s body in the 360∘ field of view under a wide variety of motions. We also propose a simple feed-forward network model to estimate 3D joint positions from 2D joint locations. The lift-up model can be used in real time yet obtains accuracy comparable to those of previous works on our new dataset. Moreover, our model is trainable with the ground truth 3D joint positions and the unit vectors toward the 3D joint positions, which are easily generated from existing publicly available 3D mocap datasets. This advantage alleviates the data collection and training burden due to changes in the camera optics and setups, although it is limited to the effect after the 2D joint location estimation.