EFFICIENT MULTI-VIEW 3D TRACKING OF ARBITRARY ROCK FRAGMENTS UPON IMPACT

This paper presents a new methodology to accurately obtain 3D rotational velocities of blocks and fragments. Four high speed cameras are used to capture the scene. An additional two tilted mirrors are used to multiply the number of views. Hence, a total of six different viewing perspectives can be used to track translational and rotational velocities in 3D. The focus in the current work is on the rotational velocities, as tracking of the translation is generally straightforward. A common outline tracking algorithm based on the visual hull is adapted. The visual hull is further meshed using triangular elements to approximate the shape of the object. This 3D reconstruction is then used to track the 3D motion of the object. However, the accuracy of the results strongly depends on the accuracy of the 3D reconstruction which is mainly influenced by the number and position of the available views. In any case, the 3D reconstruction from the visual hull is only an approximation and significant errors can be introduced which influence the tracking accuracy. Hence, an in-house post-processing algorithm based on the knowledge of the real geometry of the object, which can generally be accurately determined after a test, was developed. The improved performance of this new post-processing method is shown by controlled spinning tests. Finally, results of a real example of an impact fragmentation test are discussed.

Deep Single-View 3D Object Reconstruction with Visual Hull Embedding

3D object reconstruction is a fundamental task of many robotics and AI problems. With the aid of deep convolutional neural networks (CNNs), 3D object reconstruction has witnessed a significant progress in recent years. However, possibly due to the prohibitively high dimension of the 3D object space, the results from deep CNNs are often prone to missing some shape details. In this paper, we present an approach which aims to preserve more shape details and improve the reconstruction quality. The key idea of our method is to leverage object mask and pose estimation from CNNs to assist the 3D shape learning by constructing a probabilistic singleview visual hull inside of the network. Our method works by first predicting a coarse shape as well as the object pose and silhouette using CNNs, followed by a novel 3D refinement CNN which refines the coarse shapes using the constructed probabilistic visual hulls. Experiment on both synthetic data and real images show that embedding a single-view visual hull for shape refinement can significantly improve the reconstruction quality by recovering more shapes details and improving shape consistency with the input image.