Quantitative analysis of surface reconstruction accuracy achievable with the tsdf representation

Abstract

During the last years KinectFusion and related algorithms have facilitated significant advances in real-time simultaneous localization andmapping (SLAM) with depth-sensing cameras. Nearly all of these algorithms represent the observed area with the truncated signed distance function (TSDF). The reconstruction accuracy achievable with the representation is crucial for camera pose estimation and object reconstruction. Therefore, we evaluate this reconstruction accuracy in an optimal context, i. e. assuming error-free camera pose estimation and depth measurement. For this purpose we use a synthetic dataset of depth image sequences and corresponding camera pose ground truth and compare the reconstructed point clouds with the ground truth meshes. We investigate several influencing factors, especially the TSDF resolution and show that the TSDF is a very powerful representation even for low resolutions.