FLaME: Fast Lightweight Mesh Estimation (ICCV17)

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, which allows for an efficient, keyframeless approach to depth estimation. FLaME (Fast Lightweight Mesh Estimation) can generate mesh reconstructions at upwards of 230 Hz using less than one Intel i7 CPU core. We present results from both benchmark datasets and experiments running FLaME in-the-loop onboard a small flying quadrotor. FLaME: Fast Lightweight Mesh Estimation using Variational Smoothing on Delaunay Graphs W. Nicholas Greene and Nicholas Roy International Conference on Computer Vision (ICCV), Venice, Italy, October 2017 Paper: https://groups.csail.mit.edu/rrg/papers/greene_iccv17.pdf Code: https://github.com/robustrobotics/flame