Dense one-shot 3D reconstruction by detecting continuous regions with parallel line projection

Abstract

3D scanning of moving objects has many applications, for example, marker-less motion capture, analysis on fluid dynamics, object explosion and so on. One of the approach to acquire accurate shape is a projector-camera system, es pecially the methods that reconstructs a shape by using a single image with static pattern is suitable for capturing fast moving object. In this paper, we propose a method that uses a grid pattern consisting of sets of parallel lines. The pattern is spatially encoded by a periodic color pat tern. While informations are sparse in the camera image, the proposed method extracts the dense (pixel-wise) phase informations from the sparse pattern. As the resuit, con tinuous regions in the camera images can be extracted by analyzing the phase. Since there remain one DOF for each region, we propose the linear solution to eliminate the DOF by using geometric informations of the devices, i.e. epipo lar constraint. In addition, solution space is .finite because projected pattern consists of parallel lines with same inter vals, the linear equation can be efficiently solved by integer least square method. In this paper, the formulations for both single and multiple projectors are presented. We evaluated the accuracy of correspondences and showed the compari son with respect to the number of projectors by simulation. Finally, the dense 3D reconstruction of moving objects are presented in the experiments.

Parallel processing of grid-based one-shot structured-light system for online 3D reconstruction of moving objects

“…It means the proposed method can execute at 10Hz for rough reconstruction and at 5Hz for dense reconstruction. Since our previous method that uses color grid pattern [12] needed 3-6 seconds for each frame by CPU implementation, the proposed method with GPU implementation is 20-40 times faster than the previous approach.…”

Frequency-division-multiplexing based period-coded fringe pattern for reliable depth sensing

“…The De Bruijn sequence can be obtained by searching Eulerian circuits or Hamiltonian circuits over different kinds of De Bruijn graphs. This pseudorandom sequence has been used to encode grid-based pattern [33] in the colorspace. However, the number of colors used increases the noise sensitivity when measuring colourful scenes [32].…”

Dense 3D Reconstruction from High Frame-Rate Video Using a Static Grid Pattern

“…Since all the lines of parallel line sets are identical, correspondences between the observed projected curves and the lines on the original pattern cannot be determined by just observing the lines themselves. Such ambiguity is efficiently solved by using either coplanarity constraints [7], [8] or epipolar constraints [26], and shapes are consequently reconstructed.…”