Best Paper Awards
2017 Winner (1st place):
Hamid Laga and Hedi Tabia for
Modeling and Exploring Co-variations in the Geometry and Configuration of Man-made 3D Shape Families
2017 Winner (2nd place):
Max Budninskiy, Beibei Liu, Yiying Tong, Mathieu Desbrun for
Spectral Affine-Kernel Embeddings
2017 Winner (3rd place):
Sebstian Claici, Mikhail Bessmeltsev, Scott Schaefer, Justin Solomon for
Isometry-Aware Preconditioning for Mesh Parameterization
SGP 2017 will attribute three best paper awards.
SGP 2017 will attribute a Software Award and a Dataset Award.
Please send nominations and applications for the software award or the dataset award until 7 June 2017 to email@example.com.
2017 Winner (1):
Nicolas Mellado, Dror Aiger, Niloy J. Mitra for Super4PCS
2017 Winner (2):
Paolo Cignoni and MeshLab Team for Meshlab
To encourage the distribution of high quality software in geometry processing, since 2011 an annual award has been given for freely available software related to or useful for geometry processing. A list of recipients from previous years can be found at: http://awards.geometryprocessing.org/
The nomination should include a description of the software and its functionality as well as a justification why and to what extend the Geometry Processing community benefits from this software. Links to the code and additional material should also be included if possible. Every software project is eligible as long as:
- it can be counted as a Geometry Processing contribution
- it is available for free to academic researchers in the field (i.e. for non-commercial use)
A jury will evaluate all nominations and select a winner based on the usual quality criteria for software. The most predominant criterion, however, will be the impact, i.e. to what degree has the software affected the scientific progress in the field.
Qingnan Zhou and Alec Jacobson for Thingi10k
This award is aimed to encourage and recognize the importance of the distribution of high-quality datasets on which geometry processing algorithms are tested. We will accept nominations both for existing, well-established datasets and for new benchmarks and geometric data collections.
The dataset must be freely available (i.e., must come at no cost), and must be covered by a license allowing free academic use to be eligible for the award. We will also allow datasets that might have been obtained by collecting from different sources, rather than created from scratch (such as the Princeton Shape Benchmark), especially in the case where such datasets come with a novel challenge or problem for Geometry Processing. However, we will give preference to original datasets that have been created directly by their providers. Of course, the nominees should be relevant to the Geometry Processing community, and therefore the datasets must have some “geometric” flavor. However, another goal behind this award is to expand our understanding of what geometric datasets might entail, and therefore, we will also consider datasets that have not traditionally been used in the SGP community (for example, volumetric data, point cloud samplings of unusual objects, etc.)
The nomination should include a description the dataset and why it is worthy of the award. A jury will evaluate all nominations and select a winner.
SGP provides papers with the reproducibility stamp to recognise the effort of researchers who, in addition to publishing their paper at SGP 2017, provide a complete open-source implementation of their algorithm. For more information on the reproducibility stamp, please check