# A Survey on Deep Learning in 3D CAD Reconstruction
## 8 Metrics
### Reconstruction from Sketches to CAD Models
#### 8.2.2 Generative Reconstruction of CAD Sketches  
In the research on the generation and reconstruction of CAD sketches, [PERSON] et al. [92] proposed a machine learning model for automatically generating sketches. Following the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Dataset** & **CD \(\downarrow\)** & **HD \(\downarrow\)** & **ECD \(\downarrow\)** & **NC \(\uparrow\)** & **IR (\%) \(\downarrow\)** \\ \hline DeepCAD [10] & & 4.25 & 39.25 & 19.33 & 0.49 & 7.14 \\ HNC-CAD [81] & & 1.09 & 20.23 & 5.94 & 0.75 & 0.32 \\ Point2 cyl [30] & DeepCAD [10] & 1.00 & 20.93 & 20.45 & 0.73 & 0.0 \\ SECAD-Net [26] & & 0.42 & 9.96 & 5.54 & 0.73 & 0.38 \\ PS-CAD [49] & & 0.21 & 8.66 & 4.65 & 0.89 & 0.43 \\ \hline HNC-CAD [81] & & 1.38 & 20.61 & 9.24 & 0.52 & 0.33 \\ SECAD-Net [26] & Fusion360 [9] & 0.69 & 12.76 & 5.15 & 0.64 & 3.82 \\ PS-CAD [49] & & 0.57 & 12.0 & 5.02 & 0.67 & 1.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative comparisons of the geometric metrics (CD, HD, ECD, NC, and IR) of different methods on two different datasets. The scale of the CD, HD, and ECD is \(10^{2}\). \(\uparrow\): the higher the better, \(\downarrow\): the lower the better. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Publication** & **Precision** & **Recall** & **F1 Score** \\ \hline [PERSON] et al. [31] & CVPR’23 & 84.12 & 82.05 & 82.62 \\ [PERSON] and [PERSON] et al. [71; 74] & - & 99.64 & 26.47 & 39.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: [PERSON] et al. method compared with the traditional 3D reconstruction method based on three orthographic views. standard evaluation method of autoregressive generative models ([PERSON] et al, 2020 [72]), log-likelihood is used as the main quantitative indicator. **Log-likelihood**: Measures the accuracy of the model's predictions by calculating the log-likelihood of the observed data generated by the model. The higher the value, the more accurate the model is in predicting the data. [PERSON] et al. [93] proposed a method for modeling and synthesizing parametric CAD sketches. Quantitative evaluation was performed by measuring negative log-likelihood (NLL) and predictive accuracy on a stretched test set and through the distribution statistics of the generated sketches. **Negative Log-Likelihood (NLL)**: The negative value of the log-likelihood, which is often used to measure the deviation between model predictions and real observed data. The smaller the NLL value, the closer the model prediction is to the actual value. **Predictive Accuracy**: Refers to the proportion of correct predictions made by the model on the test data, which is used to measure the overall prediction performance of the model. The higher the value, the better the model performance. [PERSON] et al. [32] proposed an engineering sketch generation model which provides a basis for subsequent parametric CAD model synthesis and combination. The quantitative evaluation indicators of the model are listed in Table 6.  
**Bits per Vertex**: The negative log-likelihood value of each test sample vertex is measured and expressed in bits. The lower the value, the better the performance. **Bits per Sketch**: Refers to the average negative log-likelihood of each sketch, expressed in bits. The lower the value, the better the model performance. For CurveGen, we report the number of bits for vertex and curve models. **Unique**: Measures how many of the generated sketches are unique as a proportion of all generated samples. **Valid**: Measures how many of the generated sketches are valid as a proportion of all samples. Invalid sketches include those that fail to fit the curve, have more than four vertices, or have duplicate vertices. **Novel**: Measures how many of the generated sketches are novel sketches that did not appear in the training set as a proportion of all generated samples. Table 6 shows the comparative results of the generative models.