Minimizing the Multi-view Stereo Reprojection Error for Triangular Surface Meshes

By Amaël Delaunoy, Emmanuel Prados, Pau Gargallo, Jean-Philippe Pons, Peter F. Sturm. 2008

80 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Hierarchical Surface Prediction for 3D Object Reconstruction (2017, methodology): “The common methods can be broadly grouped as implicit volumetric reconstruction [7], [18], [19], [22], [38] or explicit mesh based [9], [12] approaches.”
Multi-View Stereo: A Tutorial (2015, background): “Instead of defining the error function and computing its derivatives [154, 78, 56, 55], one can also directly define “forces” as derivatives for gradient descent, based on the photometric consistency and mesh information [93].”
Multi-View Mesh Reconstruction with Neural Deferred Shading (2022, unknown): “[11, 77, 82]), yet all either assume known material or light parameters or restrict the parameter space with prior information, e.g . by assuming constant material across surfaces.”
Learned Multi-patch Similarity (2017, background): “We note that there is also a large body of work termed “multi-view reconstruction” that in fact computes depth maps from two views and focuses on integrating the corresponding 3D point clouds into implicit [7, 25] or explicit [5] surface representations.”
Learning a Hierarchical Latent-Variable Model of 3D Shapes (2017, methodology): “With respect to 3D object generation from 2D images, commonly used methods can be roughly grouped into two categories: 3D voxel prediction [45, 44, 13, 32, 5, 16] and mesh-based methods [12, 7].”
High Accuracy and Visibility-Consistent Dense Multiview Stereo (2012, unknown): “The benefits of this approach have long been recognized in mesh processing, but have seldom been demonstrated in computer vision [11], [26], [52].”