FLaME: Fast Lightweight Mesh Estimation Using Variational Smoothing on Delaunay Graphs

W. Nicholas Greene, Nicholas Roy; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 4686-4694

We propose a lightweight method for dense online monocular depth estimation capable of reconstructing 3D meshes on computationally constrained platforms. Our main contribution is to pose the reconstruction problem as a non-local variational optimization over a time-varying Delaunay graph of the scene geometry, which allows for an efficient, keyframeless approach to depth estimation. The graph can be tuned to favor reconstruction quality or speed and is continuously smoothed and augmented as the camera explores the scene. Unlike keyframe-based approaches, the optimized surface is always available at the current pose, which is necessary for low-latency obstacle avoidance. FLaME (Fast Lightweight Mesh Estimation) can generate mesh reconstructions at upwards of 230 Hz using less than one Intel i7 CPU core, which enables operation on size, weight, and power-constrained platforms. We present results from both benchmark datasets and experiments running FLaME in-the-loop onboard a small flying quadrotor.