## 2 Adaptive Spatial Feature Fusion Depth Completion
### Semi-Quantitative Visualization  
Depth maps are stored on the computer as 16-bit grey-scale images, with the value of each pixel representing depth information. On the KITTI dataset, the farthest distance of a depth map typically only reaches to about 80 m, as the sparse depth map contains information in only 5% of the pixels, and the ground truth increases to 30% of the pixels containing information. When the image is opened directly for observation, the whole map is black and it is difficult to observe the information in the depth map. In order to better observe the information in the depth map with the naked eye, the depth map needs to be quantified and colored. A common approach today is to quantize the depth map uniformly, using:  
\[P_{c}=\frac{d-d_{min}}{d_{max}-d_{min}} \tag{9}\]  
where \(P_{c}\) denotes the normalized pixel value, \(d\) denotes the depth information in the depth map, \(d_{max}\) denotes the point with the farthest distance in the depth map, and \(d_{min}\) denotes the value with the nearest distance depth in the depth map. By normalizing the depth map, it can be converted by the color map for visualization, as shown in Figure 2.  
The right of Figure 2b shows a 30% dense ground truth with a slightly visible outline. In contrast, the original sparse depth map in Figure 2b is invisible to the naked eye as only 5% of the pixels have depth information; it is simply dark. To see the depth map clearly, it needs to undergo pseudo-color coloring, as shown in Figure 2c. However, the current mean-quantization coloring methods are only applicable for observing the internal relationship of each pixel. As different images have different depth maxima and minima, this approach does not allow for a horizontal comparison of the advantages and disadvantages of each algorithm. The mean quantization method divides the pseudo-color range evenly according to the range of depth values. However, in an autonomous driving scene, the driver is more concerned with near objects than far objects. Because of this, when assigning a color map range, more color should be given to the depth range with smaller values in order to convey changes in depth information to the driver through visual effects. In this section, a novel method of non-mean quantization for depth map visualization, a semi-quantitative visualization, is proposed. As mentioned earlier, humans are very sensitive to changes in distance of one meter, but beyond ten meters, the perception of distance becomes blurred. In the images captured by the camera, the driver is not concerned with the movement of vehicles and pedestrians  
Figure 2: Comparison of original depth map and visualization. (**a**) RGB images; (**b**) original sparse depth map (**left**) and ground truth (**right**); (**c**) sparse depth map (**left**) and ground truth (**right**) after visualization. at a distance, but is more concerned with the risk of collision with vehicles and pedestrians at a closer distance. Based on this, the quantification of depth values should be guided by the idea of assigning more color to the depth range with smaller values and less color to the depth range with larger values.