## 3 Experiments
### Depth Completion Result  
The performance of the method in this paper, which has been experimentally validated on the KITTI test set, is shown in Table 2. Compared with deep completion algorithms in recent years, the method in this paper performs better in terms of the RMSE metric. Compared with methods that use multiple network models for optimizing prediction results, such as SSGP [35], the method in this paper is relatively simple to build, using a single-network architecture that only requires RGB images and depth map as input. This lightweight architectural model is more suitable for the application of autonomous driving. The method adopts an adaptive spatial feature fusion design, which improves the depth completion performance while effectively alleviating the phenomenon of inaccurate depth prediction of small objects due to the information loss problem arising from the downsampling process. We compare the effectiveness of this paper's method with other depth completion methods in recent years, as shown in Figure 3. As can be seen from the first column of results, in the area marked by the yellow rectangular box, most algorithms are unable to accurately outline the contour shape of the car, let alone the surface texture, because the vehicle is very far away from the location. In contrast, the method in this paper is able to clearly show the edge contours of the car and can clearly observe the change in depth of the front windscreen position. In contrast, the method of pNCNN (d) [6] has completely lost information about the vehicle, while the methods of TWISE [36] and ScaffFusion-SSL [37] have distorted vehicle contours and slightly rough surface textures. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Method** & **iRMSE (I/km)** & **iMAE (I/km)** & **RMSE (mm)** & **MAE (mm)** \\ \hline Experiment 1 & DepthNet & 897.587 & 368.446 & 3.212 & 2.021 \\ Experiment 2 & DepthNet+ASFF-ED & **857.191** & **254.503** & **2.817** & **1.203** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative evaluation on KITTI test set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **iRMSE (I/km)** & **iMAE (I/km)** & **RMSE (mm)** & **MAE (mm)** \\ \hline NonLearning Complete [38] & 3.63 & 1.23 & 1222.00 & 303.82 \\ VOICED [39] & 3.56 & 1.20 & 1169.97 & 299.41 \\ AdaFrame-VGG8 [40] & 3.32 & 1.16 & 1125.67 & 291.62 \\ ScaffFusion [37] & 3.32 & 1.17 & 1121.89 & 282.86 \\ SynthProj [41] & 3.53 & 1.19 & 1095.26 & 280.42 \\ VLW-DepthNet [42] & 3.43 & 1.21 & 1077.22 & 282.02 \\ KBNet [43] & 2.95 & 1.02 & 1069.47 & 256.76 \\ SynthProj [41] & 3.12 & 1.13 & 1062.48 & 268.37 \\ LW-DepthNet [42] & 2.99 & 1.09 & 991.88 & 261.67 \\ pNCNN (d) [6] & 3.37 & 1.05 & 960.05 & 251.77 \\ IR\_L2 [44] & 4.92 & 1.35 & 901.43 & 292.36 \\ ScaffFusion-SSL [37] & 3.24 & 0.88 & 847.22 & 205.75 \\ TWISE [36] & **2.08** & **0.82** & 840.20 & **195.58** \\ SSGP [35] & 2.51 & 1.09 & 838.22 & 244.70 \\ Ours & 2.73 & 1.20 & **818.42** & 252.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative evaluation on KITTI test set. Further comparing the image in the second column, the strong reflections at the location of the yellow rectangular box lose much of the image texture information while producing shadows, which causes the algorithm to produce jagged dithering information during depth map estimation. However, this does not affect the algorithm's detection of small object targets. It can be observed that although the edge parts are not smooth, the vehicle and tree contours in the depth map of this paper are more similar to the original image compared to other algorithms. Through experimental comparisons, it is verified that the method in this paper has certain advantages in small object, and the effectiveness of the ASFF-ED module for the small objects of depth completion is demonstrated.