Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds of Large Scenes with Learned Virtual View Visibility (2108.08378v1)

Abstract: We present a novel framework for mesh reconstruction from unstructured point clouds by taking advantage of the learned visibility of the 3D points in the virtual views and traditional graph-cut based mesh generation. Specifically, we first propose a three-step network that explicitly employs depth completion for visibility prediction. Then the visibility information of multiple views is aggregated to generate a 3D mesh model by solving an optimization problem considering visibility in which a novel adaptive visibility weighting in surface determination is also introduced to suppress line of sight with a large incident angle. Compared to other learning-based approaches, our pipeline only exercises the learning on a 2D binary classification task, \ie, points visible or not in a view, which is much more generalizable and practically more efficient and capable to deal with a large number of points. Experiments demonstrate that our method with favorable transferability and robustness, and achieve competing performances \wrt state-of-the-art learning-based approaches on small complex objects and outperforms on large indoor and outdoor scenes. Code is available at https://github.com/GDAOSU/vis2mesh.

An Overview of Vis2Mesh: Efficient Mesh Reconstruction from Unstructured Point Clouds

This essay offers a detailed examination of the "Vis2Mesh" framework, as presented in the paper by Song, Cui, and Qin. The method developed in this paper introduces an innovative approach for reconstructing meshes from unstructured point clouds, particularly geared towards large-scale environments. This is achieved by integrating learned virtual view visibility with traditional graph-cut based mesh generation techniques.

Core Contributions

The Vis2Mesh method advances the field by leveraging learned visibility in virtual views combined with classical optimization techniques. The authors propose a three-step neural network for visibility prediction, augmented by depth completion tasks, ensuring effective utilization of point visibility for high-quality mesh generation. The key contributions can be outlined as follows:

1. Visibility Prediction through Virtual Views: The authors introduce a novel approach to model point visibility by generating virtual views around point clouds. This method allows for an exhaustive visibility analysis which surpasses the limitations of physical views commonly used in prior methodologies.
2. Graph-cut Optimization: Building on the visibility information, the method employs an optimization problem to derive the optimal surface configuration. The authors introduce an adaptive visibility weighting term to mitigate artifacts from large incidence angle rays, resulting in more precise surface determination.
3. Scalability and Efficiency: The framework utilizes a 2D binary classification task for visibility prediction, which outperforms other learning-based approaches in terms of generalizability and efficiency. The method shows remarkable adaptability, coping well with diverse point densities and large-scale scenes.

Numerical Evaluations and Results

The experiments validate the effectiveness of Vis2Mesh with strong numerical outcomes. The method demonstrates competitive performance, excelling particularly on large-scale indoor and outdoor scenes when compared with state-of-the-art methods. The integration of both traditional graph-cut techniques and modern deep learning aspects presents a balanced trade-off between accuracy and computational tractability.

The evaluation concluded with superior F-scores and lower Chamfer distances when pitted against comparative methods, highlighting the effectiveness of this hybrid approach. Notably, Vis2Mesh yields a robust performance amidst noise and incomplete data, attributed to its novel depth completion strategy and adaptive visibility weighting.

Implications for Future Research

The Vis2Mesh proposal opens pathways for future research in large-scale 3D reconstruction. The augmentation of traditional methods with deep learning capabilities stands as a promising direction for the development of more generalizable and efficient models. Prospective research could investigate extending this framework to support dynamic environments or further refining the weighting schemes to accommodate varied sensor modalities.

Moreover, the application of such methods could span various domains within computer vision, virtual reality, and autonomous navigation, where real-time and accurate surface reconstructions are paramount.

Conclusion

In conclusion, the Vis2Mesh framework presents a significant contribution to mesh reconstruction methodologies, effectively combining deep learning with traditional optimization techniques. Its robust performance across varied scales and environments marks an advancement in the field, with promising implications for both practical applications and future theoretical development in computer vision and related areas. The authors offer a compelling narrative for integrating traditional and modern approaches to address complex reconstruction challenges efficiently.