| Budget Amount *help |
¥13,650,000 (Direct Cost: ¥10,500,000、Indirect Cost: ¥3,150,000)
Fiscal Year 2020: ¥2,860,000 (Direct Cost: ¥2,200,000、Indirect Cost: ¥660,000)
Fiscal Year 2019: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
Fiscal Year 2018: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
Fiscal Year 2017: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
|
|---|
| Outline of Final Research Achievements |
In this research, we have developed a fine-grained 3D shape-like search method based on deep learning, proposed new partial shape representations and a method for automatic annotation for objects in 3D scene. The target data is consists of a large amount of 3D objects typically seen in 3D scenes and 3D assembly databases.
First, we have developed a TVS (Tri-projection Voxel Splatting) method that can recognize 3D scenes with high precision. Second, we have developed a TBPSR (Topology Based Partial Shape Retrieval) method based on topological structure. Finally, by adding POS (Part-Of-Speech) information to the annotation during training stage, we have developed a novel automatic annotation method for highly accurate 3D scene images.
|
