Shape retrieval of non-rigid 3d human models
This work addresses shape retrieval for computer graphics and vision applications, but it is incremental as it builds on a previous benchmark.
The paper tackles the problem of distinguishing between body shapes in non-rigid 3D human models by extending a benchmark with new training data and experiments, resulting in participation from 25 methods and updated results.
3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods.