# Real-Time 3D Reconstruction Pipeline for Room-Scale, Immersive, Medical Teleconsultation
## 8 Conclusions and Future Work  
We presented a stateless surface reconstruction method, which is scalable and efficient enough to reconstruct a full room in real time with sufficient quality for immersive teleconsultation. Our method generates a mesh directly from voxelized point clouds. We extended the formulation of Ju's recursive dual-mesh traversal [17] for cases of the diagonal neighborhood and showed how to encode these cases. Like in the original dual-contouring meshing, manifoldness is not guaranteed, which appears in our application if surfaces are too close to resolve correctly in the voxel grid. Holes are only filled if the voxelization itself does not contain holes, and we presented a way of merging or separating close-by surfaces if the grid for voxelization is suboptimally aligned. We also specified additional conditions for safe simplifications, such that no interior faces are generated. Since we use a dual-meshing method, multiple detail levels with mixed resolutions for voxels are possible, and seamless connections are formed for the final mesh, which makes the method very versatile. Due to the voxel-clustering-based simplification of the octree, the output mesh is much smaller compared with other mesh reconstruction techniques, such as marching cubes or direct 2.5D meshing of all depth pixels [5; 15], particularly if the geometry consists of many flat surfaces. Moreover, since simplification is a predecessor of mesh generation, the workload for the subsequent mesh generation is significantly reduced. Given the right parameters, we can provide a reconstruction of a simplified mesh from geometry captured with six fixed, partially overlapping depth cameras in real time directly, without a canonical model or more expensive out-of-order processing steps. The quality of \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Preset Name**} & \multicolumn{3}{c}{**Frames per Second**} & \multicolumn{3}{c}{**Latency (ms)**} \\ \cline{2-7}  & **Min.** & **Avg.** & **Max.** & **Min.** & **Avg.** & **Max.** \\ \hline _unsimplified-low-detail_ & 26.1 & 28.3 & 31 & 110 & 272 & 305 \\ \hline _simplified-low-detail_ & 26.1 & 28.3 & 31.3 & 185 & 274 & 303 \\ \hline _simplified-high-detail_ & 14.5 & 24.6 & 29.6 & 99 & 111 & 192 \\ \hline _LOD_ & 17.3 & 31.8 & 51.2 & 45 & 68 & 140 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: System Performance. Full system performance using a selection of preset settings (Section 6.3.2). our reconstruction is better than applying an existing direct-meshing method directly after voxelization, but not as smooth and hole-free as implicit surface reconstruction because we still use the points obtained by voxelization directly. Currently, we use a per-pixel screen space texturing method for the final mesh to demonstrate our system (Section 3.5), which is prone to errors with slight misalignments of the calibration and requires high-quality depth images. The integration of existing work for consistent texturing [77; 78; 79] would be very beneficial to increase visual fidelity and consistency, which is vital for medical telepresence since many features and tools are too small to resolve with geometry using depth cameras. The enhancement of depth images [80; 81; 82; 83] could also significantly increase the quality of our visual result and the time warping procedure presented in FusionMLS [15] to merge depth images at different recording timestamps better. While our method generates a simplified mesh, we currently do not compress the representation during transport, requiring us to use a gigabit connection. Mesh compression [84] libraries, such as Google/Draco [85], add a lot of latency for the large-scale meshes we use. Exploiting the spatiotemporal coherence [86] would yield best compression ratios, and since our mesh is not arbitrary, but generated from an octree, the data structure could be potentially exploited to more efficiently find the relevant frame-to-frame deltas in future works.