## 2 Related Work and the Proposed Algorithm  
### Related Work  
The reconstruction process first needs a number of feature points to obtain an accurate 3D modeling of the karst. Multiple image processing algorithms, permitting the detection and extraction of region of interest are available in literature. Fast descriptors [3] are commonly used in order to speed-up classification and recognition of a specific set of keypoints as well as SIFT (Scale-Invariant Feature Transform) [4], Harris [5] and SURF (Speeded Up Robust Features) [6] descriptors. All of these feature detectors have invariant characteristics in a spatial domain. However, they are lacking quality when images undergo important modifications like underwater ones [7]. Using active systems with laser devices permits to facilitate the extraction of accurate features based on the laser characteristics. In [8], authors proposed a new laser detection technique that combines several specifications such as brightness, size, aspect ratio, and gradual change of intensity from the center of laser spot. In [9], others use a combination of motion detection, shape recognition and histograms comparison methods in order to detect the laser spot. In the same context, segmentation process appears in most of works such as [10; 11; 12; 13; 14; 15]. In fact, thresholding permits to classify the laser spot according to brightness value using a previously calculated threshold value. In [16], a new approach is proposed using a different color system that represents Hue, Saturation and Intensity (HSI) instead of RGB color system. The previous mentioned methods are sometimes not enough accurate and present some false-offs because of lighting conditions or threshold value selection. In [17], authors present an approach that permits to differentiate good and bad detections by analyzing the proportionality between the projection surface diameter and the number of classified laser pixels in a group. In [18], fuzzy rule based system adjusted by a genetic algorithm is proposed to improve the success rate of the laser pointer detection algorithm decreasing as much as possible the false-offs. In [19], authors developed an approach based on motion estimation to extract \"good\" features. They first use the Kanade-Lucas-Tomasi feature tracking algorithm to extract features at time t which are then tracked at time \(t+1\). Using the disparity map previously extracted for both time steps, tracked points that do not have a corresponding disparity at both time \(t\) and \(t+1\) are eliminated. In [20], authors were focused on feature point extraction, the first step of the sparse 3D reconstruction system, since they represent the geometrical characteristics of the scene. They propose a methodology to extract robust feature points from acoustic images and offer 3D environment reconstruction. This approach is realized in two steps: contours extraction and corners from contours feature points extraction. For the first step, canny edge detection [21] was applied based on intensity discontinuities. For the second step, an algorithm that analyzes each defined curve to mark the first and last points as feature points. These characteristic points, extracted from each image, are matched in order to estimate the camera movement and reconstruct the scene. In [22], authors used also edge detection approach to devise stereo images into blocks. A sparse disparity map is generated based on stereo block matching. They investigate for this aim, the performances and cost calculation results of different methods of matching such as Sum of Absolute Differences (SAD), Herman Weyl's Discrepancy Measure (HWDM) and Adaptive Support Windows (ASW) to compare performances of each method in estimation of disparity and maximum disparity values. In all 3D systems, depth information of the real scene is essential.