Nicolas Bonneel, David Coeurjolly, Julie Digne, Nicolas Mellado
Being able to duplicate published research results is an important process of conducting research whether to build upon these findings or to compare with them. This process is called "replicability" when using the original authors' artifacts (e.g., code), or "reproducibility" otherwise (e.g., re-implementing algorithms). Reproducibility and replicability of research results have gained a lot of interest recently with assessment studies being led in various fields, and they are often seen as a trigger for better result diffusion and transparency. In this work, we assess replicability in Computer Graphics, by evaluating whether the code is available and whether it works properly. As a proxy for this field we compiled, ran and analyzed 151 codes out of 374 papers from 2014, 2016 and 2018 SIGGRAPH conferences. This analysis shows a clear increase in the number of papers with available and operational research codes with a dependency on the subfields, and indicates a correlation between code replicability and citation count. We further provide an interactive tool to explore our results and evaluation data.
The Local Binary Pattern (LBP) is a very popular pattern descriptor for images that is widely used to classify repeated pixel arrangements in a query image. Several extensions of the LBP to surfaces exist, for both geometric and colorimetric patterns. These methods mainly differ on the way they code the neighborhood of a point, balancing the quality of the neighborhood approximation with the computational complexity. For instance, using mesh topological neighborhoods as a surrogate for the LBP pixel neighborhood simplifies the computation, but this approach is sensitive to irregular vertex distributions and/or might require an accurate surface re-sampling. On the contrary, building an adaptive neighborhood representation based on geodesic disks is accurate and insensitive to surface bendings but it considerably increases the computational complexity. Our idea is to adopt the kd-tree structure to directly store a surface described by a set of points and to build the LBP directly on the point cloud, without considering any support mesh. Following the LBP paradigm, we define a local descriptor at each point that is further used to define a global statistical Mean Point LBP (mpLBP) descriptor. When used to compare shapes, this descriptor reaches state of the art performances , while keeping a low computational cost. Experiments on benchmarks and datasets from real world objects are provided altogether with the analysis of the algorithm parameters, property and descriptor robustness.