Abstract. In this paper, we propose and evaluate various distanceaware weighting strategies to improve reconstruction accuracy of a voxelbased model according to the Truncated Signed Distance Function (TSDF), from the data obtained by low-cost depth sensors. We look at two strategy directions: (a) weight definition strategies prioritizing importance of the sensed data depending on the data accuracy, and (b) model updating strategies defining the level of influence of the new data on the existing 3D model. In particular, we introduce Distance-Aware (DA) and Distance-Aware Slow-Saturation (DASS) updating methods to intelligently integrate the depth data into the synthetic 3D model based on the distance-sensitivity metric of a low-cost depth sensor. By quantitative and qualitative comparison of the resulting synthetic 3D models to the corresponding ground-truth models, we identify the most promising strategies, which lead to an accuracy improvement involving a reduction of the model error by 10 − 35%.

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Abstract:In this paper, we propose and evaluate various distance-aware weighting strategies to increase the accuracy of pose estimation by improving the accuracy of a voxel-based model, generated from the data obtained by low-cost depth sensors. We investigate two strategies: (a) weight definition to prioritize prominence of the sensed data according to the data accuracy, and (b) model updating to determine the influential level of the newly captured data on the existing synthetic 3D model. Specifically, we propose Distance-Aware (DA) and Distance-Aware Slow-Saturation (DASS) updating methods to intelligently integrate the depth data into the 3D model, according to the distance-sensitivity metric of a low-cost depth sensor. We validate the proposed methods by applying them to a benchmark of datasets and comparing the resulting pose trajectories to the corresponding ground-truth. The obtained improvements are measured in terms of Absolute Trajectory Error (ATE) and Relative Pose Error (RPE) and compared against the performance of the original Kinfu. The validation shows that on the average, our most promising method called DASS, leads to a pose estimation improvement in terms of ATE and RPE by 43.40% and 48.29%, respectively. The method shows robust performance for all datasets, with best-case improvement reaching 90% of pose-error reduction.

Abstract:In this paper, we report on experiments on deployment of an extended distance-aware KinFu algorithm, designed to generate 3D model from Kinect data, onto depth frames extracted from stereo camera data. The proposed idea allows to suppress the Kinect usage limitation for outdoor sensing due to the IR interference with sunlight. Besides this, exploiting the stereo data enables a hybrid 3D reconstruction system capable of switching between the Kinect depth frames and stereo data depending on the quality and quantity of the 3D and visual features on a scene. While the nature of the stereo sensing and the Kinect depth sensing is completely different, the stereo camera and the Kinect show similar sensitivity to distance capturing. We have evaluated the stereo-based 3D reconstruction with the extended KinFu algorithm with the following distance aware weighting strategies: (a) weight definition to prioritize importance of the sensed data depending on its accuracy, and (b) model updating to decide about the level of influence of the new data on the existing 3D model. The qualitative comparison of the resulting outdoor 3D models shows higher accuracy and smoothness of models obtained by introduced distance-aware strategies. The quantitative analysis reveals that applying the proposed weighting strategies onto stereo datasets enables to increase robustness of the pose-estimation algorithm and its endurance by factor of two.

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