## 1 Introduction
### Existing Algorithms and Algorithms in Related Fields  
For passive stereo vision, a large research field of correspondence search algorithms exists [23], including many algorithms targeting real-time applications. However, the conditions for our active multi-shot systems are different (Table 1). We do not share many of the challenges which exist for the passive case. To find correspondences, a similarity measure is required. In the passive case, the similarity measure compares a region of pixels in the first camera with a region in the second camera [23]. In the active case, this is an option too, but more often the grey value sequences of a single pixel from each camera are compared (i.e., purely temporal) which yields higher accuracies [14]. A combination of both (a small spatial window for each image in the temporal sequence) has also been applied successfully [15]. Similarity measures for passive stereo include, for example, the Sum of Absolute Differences (SAD), the Sum of Square Differences (SSD) and the Normalized Cross Correlation (NCC) [23; 24; 25]. For the active case the NCC is by far the most popular similarity measure [14; 15; 16]. Other measures have been tried: SAD, SSD, correlation of temporal gradients, Sum of Absolute Differences of temporal gradients; but NCC yields the most accurate results [26]. In this paper, we propose to split the correspondence search into two parts: 1. a coarse correspondence search which is based on our newly developed algorithm and 2. a correspondence refinement step which uses NCC. The result from the first step is used as a initial solution for the refinement step. In this way, we can significantly improve the speed of the correspondence search without compromising the measurement accuracy. We have proposed one variant of our new algorithm \"Binary Correspondence Search (BICOS)\" in Reference [27]. Since then, we have improved it (BICOS+) and now present an evaluation of both algorithm variants. We compare it against a reference algorithm which is purely NCC based. Our algorithm uses binary features (BF) to describe the temporal brightness value sequence of a pixel. Binary features have also been proposed for correspondence search in photogrammetry between uncalibrated cameras [28; 29; 30] and have been used extensively for texture classification [31]. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Passive Stereo** & **Active Multi-Shot Stereo with Statistical Patterns** \\ \hline All information for correspondence search must be found from spatial features, for example, texture, object edges, shadows, etc. & We can rely on temporal features. For a given pixel in the left camera we can find a correspondence without looking at any other pixel in the same camera. \\ \hline Correspondences for smooth image areas without texture or other features must be guessed from surrounding image features. & The projected patterns are visible on all object parts, pixel-wise correspondences can also be found in smooth image areas. \\ \hline Spatial image features look slightly different from each camera perspective, they have a different projected geometry. The correspondence search algorithm must account for that. & If only temporal features are used (i.e., only the brightness value sequence of a single pixel), there is only minimal geometric change. \\ \hline The reflected ambient light intensity may be different at each camera view-point because the reflectivity of most objects depends on the angles towards light source and view point. & In addition to (unwanted) ambient light, the projected patterns may have a different intensity for each camera. The reflection factors for the projected light and the ambient light are different, because the angles towards the light sources are different. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data processing challenges: differences between passive stereo and active multi-shot stereo with statistical patterns.