Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks (1909.00321v1)

Abstract: Reconstructing the 3D mesh of a general object from a single image is now possible thanks to the latest advances of deep learning technologies. However, due to the nontrivial difficulty of generating a feasible mesh structure, the state-of-the-art approaches often simplify the problem by learning the displacements of a template mesh that deforms it to the target surface. Though reconstructing a 3D shape with complex topology can be achieved by deforming multiple mesh patches, it remains difficult to stitch the results to ensure a high meshing quality. In this paper, we present an end-to-end single-view mesh reconstruction framework that is able to generate high-quality meshes with complex topologies from a single genus-0 template mesh. The key to our approach is a novel progressive shaping framework that alternates between mesh deformation and topology modification. While a deformation network predicts the per-vertex translations that reduce the gap between the reconstructed mesh and the ground truth, a novel topology modification network is employed to prune the error-prone faces, enabling the evolution of topology. By iterating over the two procedures, one can progressively modify the mesh topology while achieving higher reconstruction accuracy. Moreover, a boundary refinement network is designed to refine the boundary conditions to further improve the visual quality of the reconstructed mesh. Extensive experiments demonstrate that our approach outperforms the current state-of-the-art methods both qualitatively and quantitatively, especially for the shapes with complex topologies.

• The paper introduces a progressive shaping framework that alternates mesh deformation with dynamic topology modification to improve 3D reconstruction accuracy.
• It leverages a Topology Modification Network to prune erroneous faces based on reconstruction errors, ensuring robust and precise mesh structures.
• A boundary refinement network enhances edge smoothness, demonstrating superior performance over state-of-the-art methods in key evaluation metrics.

A Comprehensive Analysis of "Deep Mesh Reconstruction from Single RGB Images via Topology Modification Networks"

The paper presented by Junyi Pan et al. tackles the intricate challenge of reconstructing 3D mesh models from single-view RGB images, a complex task in the fields of computer vision and computer graphics. Their novel contribution lies in employing a Topology Modification Network that allows for dynamic modifications in mesh topology, representing a significant advancement over existing methods restricted by predefined topologies. This paper, therefore, contains several critical insights pertaining to the methodology for improving mesh quality and accuracy through progressive mesh deformation and topology modification.

Methodological Innovations

The authors propose a unique end-to-end architecture that progresses through multiple stages of mesh deformation and topology modification. The key innovations include:

1. Progressive Shaping Framework: The framework alternates between deforming the mesh and modifying its topology, which allows for incremental improvements in mesh quality and the accurate modeling of complex topologies from a single genus-0 template mesh. This progressive method ensures robust handling of diverse shapes encountered in real-world datasets.
2. Topology Modification Network: This network predicts and prunes error-prone faces based on reconstruction errors, allowing the topology to evolve dynamically. The face-pruning process is carefully regulated by a gradient threshold, ensuring that only the most egregiously erroneous faces are removed, thus preserving the mesh's structural integrity.
3. Boundary Refinement Network: The introduction of a boundary refinement network is designed to address jagged edges resulting from face pruning. This module applies boundary regularization, enhancing the smoothness and visual quality of mesh boundaries.

Quantitative and Qualitative Outcomes

Quantitative evaluations reveal the superiority of the proposed method over other state-of-the-art approaches such as N3MR, Pixel2Mesh, and AtlasNet-25 in both Chamfer Distance (CD) and Earth Mover's Distance (EMD). The results, as stated in the paper, show significant improvements across various object categories, including complex shapes like chairs and tables, which are traditionally challenging for topology-fixed models. Qualitatively, the meshes generated by their approach display fewer artifacts, such as overlaps and self-intersections, which often plague multi-patch methods.

The paper also discusses a Poisson Surface Reconstruction technique as a post-processing step to overcome the non-closure of meshes, providing a potential pathway for applications requiring watertight models.

Implications and Future Directions

The approach has profound implications for applications in areas such as virtual/augmented reality and autonomous robotics, where accurate 3D reconstructions from minimal data inputs are critical. The ability to dynamically adapt topology while maintaining mesh integrity can lead to more realistic simulations and enhanced interaction capabilities in such environments.

Looking forward, the paper suggests enhancements through a differentiable mesh stitching operation, which could address current limitations associated with open boundaries. Additionally, improvements in neural network architectures for error prediction and boundary refinement could further bolster performance and applicability.

Conclusion

Overall, this paper makes significant strides in the domain of single-image 3D mesh reconstruction, addressing key limitations of prior methods by integrating topology modification into an end-to-end learning framework. This work exemplifies a careful balance between theoretical innovation and practical application, laying the groundwork for future developments in flexible, topology-adaptive mesh reconstruction.