## 5 Conclusions and Outlook  
In this contribution, we described our algorithm Binary Correspondence Search (BICOS) and its improved variant BICOS+. It is a coarse correspondence search algorithm for multi-shot stereo sensors with statistical pattern projection. We compared it to the NCC-based reference algorithm (NCCCOS) in terms of speed and correctness of the coarse correspondence search result. Both algorithms share the same final correspondence refinement stage and therefore yield the same final results given a correct input from the coarse correspondence stage.  
BICOS(+) is much faster than the NCCCOS algorithm. This results in a significantly reduced latency between image acquisition and availability of the 3D point cloud. While in an offline calculation scenario, this may be of little advantage, it is vital for applications like live patient monitoring.  
We can thus conclude that for applications which require short latencies or high 3D frame rates, BICOS+ is the algorithm to use, while NCCCOS is to be preferred when 3D reconstruction speed is of no concern due to its slightly better coarse search correctness. If the additional flexibility which the statistical pattern method provides is not needed (i.e., much simpler projector construction, e.g., for extended spectral ranges or high-power, high-speed projection for large fields), classical phase shift profilometry can be a good alternative because it offers similar measurement accuracy and has much lower computational requirements.  
In our future research, we want to investigate the influence of the projected patterns on the coarse correspondence search quality. Our goal is to achieve better results at shorter sequence lengths. This would allow us to reduce the total latency (of the image acquisition plus the reconstruction) even further.  
The rectified camera images which we used for our experiments are available online at [[https://drive.google.com/file/d/16E-vWnBAJGZZuj-labq4](https://drive.google.com/file/d/16E-vWnBAJGZZuj-labq4) GPqjl7 vSlq00/view?usp=sharing]([https://drive.google.com/file/d/16E-vWnBAJGZZuj-labq4](https://drive.google.com/file/d/16E-vWnBAJGZZuj-labq4) GPqjl7 vSlq00/view?usp=sharing).  
Conceptualization, P.D., S.H., M.L.; methodology, P.D.; software, P.D.; validation, P.D.; formal analysis, P.D.; investigation, P.D.; resources, P.K., G.N.; data curation, P.D.; writing--original draft preparation, P.D.; writing--review and editing, P.D., S.H., M.L., P.K.; visualization, P.D.; supervision, G.N.; project administration, P.D.; funding acquisition, P.K., G.N.  
This research received no external funding.  
The authors declare no conflict of interest.  
Abbreviations  
BICOS & Binary Correspondence Search  
BICOS+ & Improved Binary Correspondence Search  
GOBO & GOes Before Optics (rotating slide)  
NCC & Normalized Cross Correlation  
NCCCOS & NCC-based Correspondence Search (the reference algorithm)  
NEBF & Number of Equal Binary Features  
NIR & Near-InfraRed light  
SAD & Sum of Absolute Differences  
Mathematical Symbols  
\(r\) & row in a rectified image  
\(c_{L}\) & column in a rectified image of the left camera  
\(c_{R}\) & column in a rectified image of the right camera  
\(d\) & the disparity between an object point in a left and in a right rectified image \(d=c_{L}-c_{R}\)  
\(\vec{d}=a_{1},...,a_{n}\)  
\(\vec{b}=b_{1},...,b_{n}\)  
\(A\) & ambient light  
factor representing the difference in camera sensitivity and  
\(s\) & reflectivity of the measured object towards the left and the right camera