# Add new tracks that are only present in the new frames of GraphB
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Image Dataset \#3** & & \\ \hline
**Input** & **Proposed** & **Depth** & **SfM**[19] & **MVS**[22] \\
**Image** & **Method** & **Map** & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._model refinement through non-rigid surface deformation. The approach involved the application of local model-to-model surface loop closure optimization as often as possible to stay close to the mode of the map distribution while utilizing global loop closure to recover from arbitrary drift and maintain global consistency. In [45], a robust approach was proposed for reconstructing indoor scenes from RGB-D videos. The approach combined the geometric registration of scene fragments with robust global optimization-based online processes. The key idea involved addressing the issue of geometric registration errors caused by sensor noise and aliasing of geometric detail. The optimization approach was found to disable erroneous geometric alignments, even when they outnumbered correct ones, by utilizing a global optimization framework based on online processes to identify discontinuities and enable robust estimation, leading to improved accuracy in the reconstructed scene models. In [46], a simple and effective single-shot 3D reconstruction approach was presented using a grid pattern-based structured-light vision method. The system consisted of a camera and a low-cost LED projector. The grid pattern-based structured-light model was combined with the camera pinhole model and multiple light plane constraints. The tangential distortion and radial distortion were compensated for. By not using the epipolar constraint, the error propagation caused by the low calibration accuracy of the stereo vision model was avoided. The grid pattern consisted of a number of intersecting light stripes. The common algorithms for stripe center extraction were inaccurate. However, the intersections of the grid lines could be precisely extracted. Following the topological distribution of the intersections, an effective extraction method was investigated to gather the sub-pixel centers on the grid strips. The white circles on the calibration board could disturb the extraction. To solve the problem, the gray value of the ellipse region on the input image was properly and automatically attenuated. A calibration method based on the coplanar constraint was presented, which could be implemented in one step. In [47], an efficient covisibility-based incremental SfM method was proposed to improve reconstruction efficiency for both unordered and sequential images. The method exploited covisibility and registration dependency to describe image connections, enabling a unified framework for reconstructing various image types. This approach was shown to be three times faster than state-of-the-art methods on feature matching with a faster order of magnitude on reconstruction, without sacrificing accuracy. In [48], a new method was introduced to efficiently plan viewpoints and trajectories for 3D reconstruction using a quadcopter with an RGB camera in outdoor environments. The method maximized information gain while minimizing travel distance, respecting the limited flight time of quadcopters. It used a hierarchical volumetric representation to distinguish between unknown, free, and occupied space, and handled occlusions efficiently. The method also utilized free-space information to avoid obstacles and plan collision-free flight paths. The authors demonstrated the effectiveness of their method through compelling 3D reconstructions and a thorough quantitative evaluation. In [49], a novel approach was proposed to enhance surface reconstruction in urban environments by integrating aerial and ground images. The key innovation was based on leveraging photogrammetric mesh models for aerial-ground image matching, overcoming the challenges posed by significant viewpoint and illumination differences.