# A Survey on Deep Learning in 3D CAD Reconstruction
## 8 Metrics
### Reconstruction from Other Forms to CAD Models
#### 8.3.2 Reconstruction of 3D CAD Models from Other Forms  
[PERSON] et al. [10] proposed the first method to model 3D shape generation as a sequence of CAD operations. The generative model uses chamfer distance (CD) as one of the evaluation metrics, which is widely used in many generative models based on discrete representations (such as point clouds) [122; 123; 124]. The accuracy of the command execution is evaluated by command accuracy (ACCcmd) and parameter accuracy (ACCparam), and the efficiency of the model is also taken into account. Due to the lack of similar CAD generative models at the time, the authors introduced multiple custom variants for comparative experiments to verify the effectiveness of their data encoding method and training method. These variants include the following: Alt-Rel, which is used to express the position of the curve relative to the position of the previous curve in the loop; Alt-Trans, which adds the position information of the loop initial position and the sketch plane origin in the stretch command; Alt-ArcMid, which uses the start, end, and middle points of the arc to define the arc curve; Alt-Regr, which directly regresses all CAD command parameters through the mean square error loss function; and DeepCAD + Aug, which introduces randomly combined CAD command sequences as a data augmentation strategy during the training phase. Specific experimental results are shown in Table 8.  
**Chamfer Distance (CD)**: Calculates the distance metric between two sets of points. It is often used to evaluate the similarity between the generated point cloud and the real point cloud. The smaller the value, the closer the generated point cloud is to the real point cloud.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **Publication** & **COV\% \(\uparrow\)** & **MMD \(\downarrow\)** & **JSD \(\downarrow\)** & **Novel\% \(\uparrow\)** & **Unique\% \(\uparrow\)** & **Valid\% \(\uparrow\)** \\ \hline BrepGen [84] & CVPR’24 & 71.26 & 1.04 & 0.09 & 99.8 & 99.7 & 62.9 \\ DeepCAD [10] & ICCV’21 & 65.46 & 1.29 & 1.67 & 87.4 & 89.3 & 46.1 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Quantitative evaluation of BrepGen and DeepCAD for unconditional generation comparison. \(\uparrow\): the higher the better, \(\downarrow\): the lower the better.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **ACCcmd \(\uparrow\)** & **ACCparam \(\uparrow\)** & **Median CD \(\downarrow\)** & **Invalid Ratio \(\downarrow\)** \\ \hline DeepCAD [10] + Aug & 99.50 & 97.98 & 0.752 & 2.72 \\ DeepCAD [10] & 99.36 & 97.47 & 0.787 & 3.30 \\ Alt-ArcMid & 99.34 & 97.31 & 0.790 & 3.26 \\ Alt-Trans & 99.33 & 97.56 & 0.792 & 3.30 \\ Alt-Rel & 99.50 & 97.66 & 0.863 & 3.51 \\ Alt-Regr & - & - & 2.142 & 4.32 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Quantitative evaluation of DeepCAD autoencoder. \(\uparrow\): the higher the better, \(\downarrow\): the lower the better.  
**Command Accuracy (ACCcmd)**: Measures the proportion of each operation in the generated CAD operation sequence that is correctly executed. The higher the value, the more accurate the CAD operation generated by the model.  
**Parameter Accuracy (ACCparam)**: A measure of the closeness between the generated CAD operation parameters (such as size, position, etc.) and the real values. The higher the value, the closer the generated CAD model is to the real design target in terms of parameters.  
**Invalid Ratio**: The proportion of the generated CAD model that cannot be successfully converted into a valid point cloud. It measures the effectiveness of model generation. The lower the value, the higher the quality of the generated CAD model.