# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 4 Experiments
### Experimental
#### 4.3.3 KITTI Dataset  
To address the verification requirements for 3D point cloud completion tasks in real-world scenarios, this study employs the challenging KITTI autonomous driving dataset for experimental analysis. Jointly constructed by the Karlsruhe Institute of Technology and the Toyota Technological Institute, this dataset is acquired through vehicular-mounted multi-sensor systems (including 64-beam rotating LiDAR, stereo vision cameras, IMU, and GPS) in complex urban environments, which comprehensively record 3D spatial information and multi-modal sensory data in dynamic traffic scenarios.  
Based on officially provided 3D bounding box annotations, vehicle instances are extracted from full-scene point clouds. After density filtering (\(>\)5 points/instance) and manual verification, we ultimately construct a test set containing 2401 car samples. Compared with synthetic datasets, KITTI's LiDAR-scanned data exhibit two significant distinctive characteristics: (1) Affected by sensor physical limitations and environmental occlusions, target point clouds demonstrate non-uniform sparse distribution characteristics, with an average point density of 23.7 \(\pm\) 6.5 points/sample, representing merely 21.3% of ShapeNet synthetic data. (2) The absence of complete ground truth point clouds makes it challenging to directly apply fully supervised evaluation paradigms.  
To address the challenge of missing ground truth data, this study adopts a dual-metric evaluation framework inspired by GRNet [27]. The quantitative analysis comprises the following: (i) Fidelity Distance (FD), defined as the average nearest-neighbor distance from each point in the input cloud to the completion result, which constrains geometric feature preservation during completion; (ii) Minimum Matching Distance (MMD), implemented by retrieving the ShapeNetCars sample with minimal Chamfer Distance (CD) to the completion result, leveraging external prior knowledge to validate semantic rationality. This hybrid evaluation mechanism effectively mitigates assessment bias caused by ground truth absence while simultaneously addressing dual objectives: geometric consistency and shape integrity.  
As quantitatively demonstrated in Table 3, LNDM-Net achieves dual-metric superiority on the KITTI real-scanned dataset with FD = 2.91 \(\times\) 10\({}^{-2}\) and MMD = 6.57 \(\times\) 10\({}^{-2}\), exhibiting 64.3% (FD) improvement in geometric fidelity and 3.1% (MMD) enhancement in shape plausibility compared to GR-Net [27]. Figure 7 visually confirms the model's sparse-input robustness, even under extreme conditions with point cloud density below  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{\(-\)} & \multicolumn{1}{c}{\(-\)} \\ \cline{2-3}
**Category** & \multicolumn{2}{c}{\(\mathbf{d_{CD}(S1\to S2)/_dCD_}\) (S2\(\to S1\))} \\ \cline{2-3}  & **PF-Net [17]** & **LNDM-Net** \\ \hline Chair & 2.537/1.848 & **2.074/1.839** \\ Table & **2.184/1.928** & 2.235/1.934 \\ Monitor & 2.229/1.876 & **2.206/1.775** \\ Stool & **1.998**/1.828 & 2.074/**1.824** \\ Speaker & **0.435**/0.435 & 0.456/**0.429** \\ Sink & 3.228/3.332 & **3.138/3.238** \\ Barrel & 1.219/1.013 & 1.247/0.997 \\ Cabinet & 1.198/1.257 & **1.136/1.337** \\ Bookshelf & 3.946/4.054 & **3.929/3.768** \\ Sofa & 3.228/3.332 & **2.870/3.248** \\ \hline \hline \end{tabular}  
* **Note:** The bolded font represents the optimal value for each row.  
\end{table}
Table 2: Experimental comparison between LNDM-Net and PF-Net on the ShapeNet dataset.  
8 points/sample, and LNDM-Net successfully reconstructs complete vehicular topology, including continuous structural expressions of door seams and roof curvature (see Figure 7). Benefiting from fine-tuning based on the pre-trained model from PCNCars, our approach effectively preserves synthetic data detail-generation capabilities while adapting to non-uniform distribution patterns in real-world scans. Both qualitative visualization and quantitative analysis collectively validate LNDM-Net's practical value in real-world 3D perception systems.