Speckle dots have the advantage of easy projection, which makes them good candidate features of structured light (SL) cameras, such as Kinect v1. However, they generally yield poor accuracy due to block matching. To improve their accuracy, this paper proposes a dot-coded SL, the coding information of which is added into dot distribution. Some of the dots are arranged regularly to provide easy-to-locate corner features, while others are specially designed to form different shapes of unique identification. A Gaussian-cross module and a simplified ResNet have been proposed to conduct robust decoding. Various experiments are performed to verify the accuracy and robustness of our framework.

No abstract

No abstract

3D vision technology has been gradually applied to intelligent terminals ever since Apple Inc. introduced structured light on iPhoneX. At present, time-of-flight (TOF) and laser speckle-based structured light (SL) are two mainstream technologies applied to intelligent terminals, both of which are widely regarded as efficient dynamic technologies, but with low accuracy. This paper explores a new approach to achieve accurate depth recovery by fusing TOF and our previous work—dot-coded SL (DCSL). TOF can obtain high-density depth information, but its results may be deformed due to multi-path interference (MPI) and reflectivity-related deviations. In contrast, DCSL can provide high-accuracy and noise-clean results, yet only a limited number of encoded points can be reconstructed. This inspired our idea to fuse them to obtain better results. In this method, the sparse result provided by DCSL can work as accurate “anchor points” to keep the correctness of the target scene’s structure, meanwhile, the dense result from TOF can guarantee full-range measurement. Experimental results show that by fusion, the MPI errors of TOF can be eliminated effectively. Dense and accurate results can be obtained successfully, which has great potential for application in the 3D vision task of intelligent terminals in the future.