RGB-D Object Dataset
The below table sumarizes the results from the different algorithms. All algorithms are evaluated on the same subset of the RGB-D Object Dataset (i.e. subsampled every 5th video frame). For more details on the experimental setting, see our paper.
Category Recognition
|
Methods
|
Papers |
RGB |
Depth |
RGB-D |
|
SIFT + Texton + Color Histogram + Spin Images + 3D Bounding Boxes
|
ICRA11A |
74.5 |
64.7 |
83.8 |
| Sparse Distance Learning
| ICRA11B |
78.6 |
70.2 | 85.4 |
|
RGB-D Kernel Descriptors
|
IROS11 |
80.7 |
80.3 |
86.5 |
|
Hierarchical Matcing Pursuit
|
ISER12 |
82.4 |
81.2 |
87.5 |
Instance Recognition (leave-sequence-out)
|
Methods
|
Papers |
RGB |
Depth |
RGB-D |
|
SIFT + Texton + Color Histogram + Spin Images + 3D Bounding Boxes
|
ICRA11A |
60.7 |
46.2 |
74.8 |
|
RGB-D Kernel Descriptors
|
IROS11 |
90.8 |
54.7 |
91.2 |
|
Hierarchical Matcing Pursuit
|
ISER12 |
92.1 |
51.7 |
92.8 |
Object Pose Estimation
|
Methods
|
Papers |
Median Pose Error |
Median Pose Error (C) |
Median Pose Error (I) |
|
Object Pose Tree (OPTree)
|
AAAI11 |
62.6 |
51.5 |
30.2 |
|
OPTree + Hierarchical Matcing Pursuit
|
ISER12 |
20.0 |
18.7 |
18.0 |
