# A Lightweight Model for 3D Point Cloud Object Detection
## 4 Experiment
### Quantitative Evaluation  
**Experiment A. ** In order to verify the feasibility of the lightweight model design in this paper, three different lightweight 3D sparse convolution layer modules are designed for ablation experiments; the experiments are performed on the KITTI dataset. The first design idea uses the \(1\times 1\times 1\) filter to process the input data, the number of which is \(S\) (where \(S<C\)); it can reduce the dimension of the input data. Then, the output of the upper layer passes through a \(1\times 1\times 1\) filter and a \(3\times 3\times 3\) filter, respectively, the number of which is \(C^{\prime}/2\). At the end, the \(H\times W\times D\times C^{\prime}\) feature map is concatenated from the output of two group convolutions. The second design idea uses \(3\times 3\times 3\) depth-wise sparse 3D convolution to convolve the \(H\times W\times D\times C\) input channel by channel, the number of which is \(C\), and then uses \(1\times 1\times 1\) point-wise sparse 3D convolution to weight the combination of the output of the previous layer in the depth direction, the number of which is \(S\). The third detailed design idea is described in Section 3.1. The experimental results are shown in Table 1. Comparing SECOND with LW-SECOND-1, it can be seen that the calculation amount is reduced by 17.4 G, the number of parameters is reduced by 1.72 M, and the mAP is reduced by 8%. By comparing SECOND and LW-SECOND-2, it can be seen that the calculation amount is reduced by 49.6 G, the parameter amount is reduced by 4.41 M, and the mAP is reduced by 12.1%. Comparing SECOND and LW-SECOND-3, it can be seen that the calculation amount is reduced by 51.0 G, the parameter amount is reduced by 4.67 M, and the mAP is reduced by 16.4%. Table 1 shows the FLOPs, parameters, and mAP for the object detection algorithm composed of three lightweight 3D sparse convolution layer modules designed in this paper. The three design methods have different degrees of reduction in the FLOPs and parameters, indicating that the lightweight 3D sparse convolution layer module explored in this paper is feasible and that the third idea is more compressed and better. It shows that the use of group convolution and factorized convolution can effectively compress the model. However, they are accompanied by a certain degree of accuracy reduction. **Experiment B. ** Knowledge distillation loss is used to supervise the training of the lightweight 3D point cloud object detection algorithm. In order to prove that using the three-part knowledge distillation loss has the best effect on the performance improvement of the algorithm, the ablation experiments are performed on KITTI and nuScences. Regarding the experiments on KITTI, we report the average precision calculated at 40 sampling recall locations for BEV object detection and 3D object detection, respectively. The implementation details of the experiment are the same as those in Section 4.2. The experimental results are shown in Table 2. The table shows the results of BEV detection and 3D detection in the KITTI dataset. Taking the test results for BEV detection as an example, we compare the SECOND and LW-SECOND-3. The accuracy is reduced from 63.3% to 48.7%. It can be seen that model compression with the lightweight model design reduces the detection accuracy of the algorithm. Compared with LW-SECOND-3 and LW-SECOND-3\({}^{\prime}\), the detection accuracy improves from 48.7% to 48.8%. It can be seen that there is a certain effect, but it is not obvious, indicating that it is not enough to use only the \(L_{\text{out}}\) constrained student model to simulate the teacher model. Comparing the detection results of LW-SECOND-3\({}^{\prime}\) and LW-SECOND-3\({}^{\prime\prime}\), it can be seen that the detection accuracy of \(L_{\text{feature}}\) improves from 48.8% to 53.0% after constraint training, indicating that the feature map of the simulated teacher model has a better effect on the prediction output.