## 4 Results  
We evaluated the functionality of our proposed method firstly by means of an experimental setup consisting of a measurement chamber, a CNC (Computerized Numerical Control) positioning unit and a compressor nebulizer (Hangsun CN560) to simulate the real ambient conditions existing in real fermentation chambers. To obtain static measurement objects, we produced salt dough pieces, which were hardened and hence keep their shape during the whole measurement procedure. We measured the topology of the chamber interior containing different dough shapes for two minutes continuously, first without the addition of aerosol, and segmented the points belonging to a certain dough object by means of our instance segmentation neural network. Figure 7 shows the extracted dough surface measurements of two different dough shapes laid over images taken manually from the same perspective as from the sensor system. For better visualization, we reconstructed the surface structure by means of a subdivision surface modifier to obtain a denser mesh. The illustration clarifies the limitations of our measurement system regarding the measurable surface part of the objects. Due to the surface curvature of the dough pieces, the reflections of the emitted light pulses outside of the red area do not reach the sensors receiving optics and hence do not generate a measurement point. To analyze the impact of a present aerosol atmosphere, we measured dough objects with different shapes ones with and ones without induced aerosol. We again segmented the measured dough surfaces and apply a subdivision surface modifier whereby we increased the number of points with a factor of approximately 100. The resulting surface meshes allowed a superimposition to examine the deviations. Figure 8 shows the meshes of three different dough pieces. Figure 7: Surface measurement laid over scene images. It is observable that the aerosol particles do not have an impact on the quality of the measurement. Both measurements map a comparable surface area of the dough pieces without large deviations. By applying the iterative closest point algorithm of the point clouds generated with and the one without aerosol addition, we obtained a quality measure of the similarity. All point cloud pairs achieved a Euclidean fitness score smaller than one, which proves the high congruence of measurements with and without added aerosol. Due to measurement inaccuracies of our measurement system, a certain degree of variance is not avoidable. So-called Mie scattering does not occur because the wavelength of the used LIDAR is, with ~785 nm, much smaller than the aerosol particle size of around 10 \(\upmu\)m [31]. The effect of optical scattering is not observable in the measurements, which could stem from the weak signal strength of the beams reflected from the particles. In comparison to our proposed method, the existing monitoring methods using optical measurement techniques are not able to work properly under the influence of aerosol. For example, the method used in [12] would not be able to catch an image by a digital camera because the light of the object would not reach the camera sensor. The structured light technique described in [4] might be able to obtain some information due to the strength of the line laser but only when being used on a short distance. After proving the suitability of our measurement system, we evaluated the quality of our model fitting algorithm to determine the volume of dough. Therefore, we performed ten measurements of our chamber interior with ten different hardened dough pieces, each a member of either the class round or long, and determine the real volume values by means of the water displacement method to obtain a reference value. We fit superellipsoidal models to the segmented dough surface parts and compared the accuracies to the determined reference values. Thereby, the rheological properties of dough have to be taken into consideration.