Fast Dense 3D Reconstruction Using an Adaptive Multiscale Discrete-Continuous Variational Method

Abstract

We present a system for fast dense 3D reconstruction with a hand-held camera. Walking around a target object, we shoot sequential images using continuous shooting mode. High-quality camera poses are obtained offline using structure-from-motion (SfM) algorithm with Bundle Adjustment. Multi-view stereo is solved using a new, efficient adaptive multiscale discrete-continuous variational method to generate depth maps with sub-pixel accuracy. Depth maps are then fused into a 3D model using volumetric integration with truncated signed distance function (TSDF). Our system is accurate, efficient and flexible: accurate depth maps are estimated with sub-pixel accuracy in stereo matching; dense models can be achieved within minutes as major algorithms parallelized on multi-core processor and GPU; various tasks can be handled (e.g. reconstruction of objects in both indoor and outdoor environment with different scales) without specific hand-tuning parameters. We evaluate our system quantitatively and qualitatively on Middlebury benchmark and another dataset collected with a smartphone camera.