Metro: Measuring Error on Simplified Surfaces

By Paolo Cignoni, C. Rocchini, Roberto Scopigno. 1998

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Citations in the corpus

A Papier-Mache Approach to Learning 3D Surface Generation (2018, positive): “To account for mesh connectivity, we compared the output and ground-truth meshes using the “Metro” criteria using the publicly available METRO software [7], which is the average Euclidean distance between the two meshes.”
T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects (2017, methodology): “The Metro software by Cignoni et al. [8] was used to measure the model differences.”
Efficient simplification of point-sampled surfaces (2002, methodology): “Similar to the Metro tool [4], we use a sampling approach to approximate surface error.”
Hierarchical mesh segmentation based on fitting primitives (2006, methodology): “The error values reported in the box have been computed through the publicly available Metro tool [6].”
Making papercraft toys from meshes using strip-based approximate unfolding (2004, methodology): “The RMS (root mean square) errors as measured by Metro [Cignoni et al. 1998] between the original models and those optimized by our method are 0.”
Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z) (2013, methodology): “Surface change is calculated by the distance between a point cloud and a reference 3D mesh or theoretical model (Cignoni and Rocchini, 1998), see also Monserrat and Crosetto (2008) and Olsen et al.”
CHANGE DETECTION ON POINTS CLOUD DATA ACQUIRED W ITH A GROUND LASER SCANNER (2005, methodology): “Point-to-mesh and mesh-to-mesh distances have been very well studied and demonstrated by academic software such as Metro (P. Cignoni et al. 1998) or Mesh (N. Aspert et al. 2002).”
Progressive geometry compression (2000, methodology): “For triangulations this distance can be computed using the METRO tool [3].”
High-Pass Quantization for Mesh Encoding (2003, unknown): “By considering a visual error metric between meshes, that takes into account not only the Euclidean distance between corresponding vertices (or the “Metro” distance [Cignoni et al. 1998]) but also the smoothness error, we argue that low-frequency displacements in the surface geometry are less notice”
Fast and Effective Feature-Preserving Mesh Denoising (2007, background): “These include 3D distance metrics [15], [33], [34], [35], [36], tangential error metrics [34], curvature error metrics [34], normal error metrics [15], [17], [35],”
SUM: A Benchmark Dataset of Semantic Urban Meshes (2021, methodology): “We use Montecarlo sampling [44] to generate randomly uniform dense samples, and we further prune these samples according to Poisson distributions [45] and assign the colour via searching the nearest neighbour from the textures.”