| Title |
Auto-refining 3D mesh reconstruction algorithm from limited angle depth data / |
| Authors |
Kulikajevas, Audrius ; Maskeliunas, Rytis ; Damasevicius, Robertas ; Krilavicius, Tomas |
| DOI |
10.1109/ACCESS.2022.3143467 |
| Full Text |
|
| Is Part of |
IEEE Access.. Piscataway, NJ : IEEE. 2022, vol. 10, p. 87083-87098.. ISSN 2169-3536 |
| Keywords [eng] |
three-dimensional displays ; robot sensing systems ; training ; neural networks ; adversarial machine learning ; human factors ; Solid modeling ; human shape reconstruction ; pointcloud reconstruction ; adversarial auto-refinement |
| Abstract [eng] |
3D object reconstruction is a very rapidly developing field, especially from a single perspective. Yet the majority of modern research is focused on developing algorithms around a single static object reconstruction and in most of the cases derived from synthetically generated datasets, failing or at least working insufficiently accurately in real-world data scenarios, regarding morphing the 3D object's restoration from a deficient real world frame. For solving that problem, we introduce an extended version of the three-staged deep auto-refining adversarial neural network architecture that can denoise and refine real-world depth sensor data current methods for a full human body pose reconstruction, in both Earth Mover's (0.059) and Chamfer (0.079) distances. Visual inspection of the reconstructed point-cloud proved future adaptation potential to most of depth sensor noise defects for both structured light depth sensors and LiDAR sensors. |
| Published |
Piscataway, NJ : IEEE |
| Type |
Journal article |
| Language |
English |
| Publication date |
2022 |
| CC license |
|
