3D Object Recognition by Caffe 〜 20-class classification 〜

Introduction

　In the previous page, a new method for a 3D object recognition was proposed. The method was applied to a 10-class classification and a classification accuracy of 90.2% was achieved. In this page, the method is evaluated on a 20-class classification. It is shown that a high classification accuracy of 95.3% is reached.

Dataset

　The pre-trained CNN model (bvlc_reference_caffenet.caffemodel) that Caffe provides is fine-tuned using a dataset consisting of 20 categories which are chosen among the ModelNet40 dataset. These categories are shown below.

1. airplane
2. bathtub
3. bed
4. bench
5. bookshelf
6. bottle
7. bowl
8. car
9. chair
10. cone
11. cup
12. curtain
13. desk
14. door
15. dresser
16. flower_pot
17. glass_box
18. guitar
19. keyboard
20. lamp

Each category has training and testing 3D models which are in Object File Format (OFF). The numbers of the models in categories are as follows:

label	name	the number of trainings	the number of testings
0	airplane	626	100
1	bathtub	106	50
2	bed	515	100
3	bench	173	20
4	bookshelf	572	100
5	bottle	335	100
6	bowl	64	20
7	car	197	100
8	chair	889	100
9	cone	167	20
10	cup	79	20
11	curtain	138	20
12	desk	200	86
13	door	109	20
14	dresser	200	86
15	flower_pot	149	20
16	glass_box	171	100
17	guitar	155	100
18	keyboard	145	20
19	lamp	124	20
		5114	1202

Results of CNN and Classification

　The fine-tuning yields a high recognition accuracy of 93% as shown below.

As described in the previous page, a 3D model yields 20 gray images. The fine-tuned CNN is applied to each of them and 20 labels are obtained per 3D model. The final label is decided by majority vote. This algorithm is evaluated on those 3D models belonging to the test phase whose number is 1202 as shown above. The classification accuracy of 95.3% is reached.