Classification and performance evaluation of different aggregation costs for stereo matching
Preliminary experimental results
This table presents the results obtained by the evaluated algorithms on the Middlebury Dataset [11]. For all the algorithms, the local cost measure adopted is the Truncated Absolute Differences (TAD) computed on RGB values (for additional results concerning different measures, please refer to the sub-menu on top of this page).
The table allows to interactively explore the results:
- by clicking on the algorithm name you can view some extracted supports (5 points on Tsukuba and 6 point on Teddy).
- by clicking on the error percentages you can view the corresponding disparity maps.
- It is possible to sort the table according to each column.
All the tuned parameter values for each algorithm which were used to produce the experimental results shown in this table are available here.
NOTE: all variants of algorithm Multiple Windows were implemented withouth the use of incremental schemes (Box-Filtering, Integral Images, ..). Hence the reported processing times concerning that algorithm are higher than those achievable by means of any of such techniques.
Algorithm | Rank Accuracy |
Tsukuba nonocc |
Tsukuba disc |
Venus nonocc |
Venus disc |
Teddy nonocc |
Teddy disc |
Cones nonocc |
Cones disc |
Rank Time |
Time Teddy (hh:mm:ss) |
Avg. Rank |
||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Locally Consistent + Fast Bilateral 39_3 [16] | 1.00 | 1.77 | 1 | 5.92 | 1 | 0.27 | 1 | 1.77 | 1 | 9.30 | 1 | 17.90 | 1 | 4.75 | 1 | 10.50 | 1 | 11 | 00:00:37 | 6.00 |
Fast Bilateral 39_3 [15] | 3.00 | 2.95 | 3 | 8.69 | 4 | 1.15 | 3 | 6.64 | 4 | 10.70 | 4 | 20.80 | 2 | 5.23 | 2 | 11.40 | 2 | 10 | 00:00:28 | 6.50 |
Segment support [10] | 3.13 | 2.15 | 2 | 7.22 | 2 | 1.38 | 4 | 6.25 | 3 | 10.54 | 2 | 21.23 | 3 | 5.83 | 5 | 11.83 | 4 | 16 | 00:30:38 | 9.56 |
Locally Consistent + Fixed Window [16] | 3.25 | 3.07 | 4 | 9.63 | 5 | 0.66 | 2 | 5.11 | 2 | 10.60 | 3 | 21.80 | 4 | 5.30 | 3 | 11.60 | 3 | 9 | 00:00:15 | 6.13 |
Fast Bilateral 45_5 [15] | 5.38 | 3.34 | 5 | 9.99 | 6 | 2.11 | 5 | 6.72 | 5 | 11.50 | 6 | 21.80 | 4 | 6.81 | 6 | 13.80 | 6 | 8 | 00:00:14 | 6.69 |
Adaptive weight [14] | 6.63 | 4.66 | 10 | 8.25 | 3 | 4.61 | 8 | 13.30 | 10 | 12.70 | 8 | 22.40 | 5 | 5.50 | 4 | 11.90 | 5 | 14 | 00:17:01 | 10.31 |
Fast Bilateral 49_7 [15] | 6.75 | 3.99 | 6 | 12.30 | 7 | 3.01 | 7 | 8.42 | 6 | 12.30 | 7 | 23.00 | 6 | 7.50 | 7 | 15.10 | 8 | 5 | 00:00:09 | 5.88 |
Variable Windows [12] | 8.50 | 4.28 | 7 | 14.26 | 11 | 5,99 | 10 | 9.17 | 7 | 13.48 | 9 | 24.69 | 8 | 7.87 | 9 | 14.94 | 7 | 9 | 00:00:15 | 8.75 |
Segmentation based [5] | 8.75 | 4.53 | 8 | 13.10 | 8 | 6.91 | 12 | 17.25 | 13 | 10.94 | 5 | 24.09 | 7 | 7.67 | 8 | 16.16 | 9 | 2 | 00:00:02 | 5.38 |
Fast Bilateral 45_9 [15] | 9.38 | 4.60 | 9 | 13.70 | 9 | 5.42 | 9 | 10.60 | 8 | 13.90 | 10 | 24.80 | 9 | 9.47 | 11 | 17.70 | 10 | 4 | 00:00:05 | 6.69 |
Shiftable Windows [11] | 14.13 | 7.58 | 17 | 21.61 | 19 | 7.79 | 15 | 13.93 | 11 | 17.19 | 13 | 29.78 | 10 | 10.27 | 13 | 22.12 | 15 | 6 | 00:00:12 | 10.06 |
Reliability [8] | 14.50 | 5.71 | 11 | 22.01 | 21 | 2.87 | 6 | 10.71 | 9 | 16.82 | 12 | 32.22 | 16 | 14.40 | 21 | 24.65 | 20 | 15 | 00:18:07 | 14.75 |
Multiple Windows (9W)* [7] | 15.38 | 8.39 | 19 | 20.75 | 17 | 8.05 | 16 | 24.38 | 18 | 17.51 | 14 | 31.67 | 13 | 10.17 | 12 | 21.60 | 14 | 3 | 00:00:04 | 9.19 |
Gradient Guided [6] | 15.63 | 6.68 | 13 | 14.19 | 10 | 11.10 | 18 | 30.02 | 24 | 19.17 | 15 | 31.19 | 12 | 12.58 | 17 | 22.46 | 16 | 4 | 00:00:05 | 9.81 |
Multiple Windows (25W)* [7] | 15.63 | 6.51 | 12 | 21.62 | 20 | 6.85 | 11 | 19.14 | 14 | 19.21 | 16 | 32.06 | 14 | 13.55 | 19 | 24.55 | 19 | 7 | 00:00:13 | 11.31 |
Multiple Windows (5W)* [7] | 15.88 | 9.56 | 22 | 15.85 | 12 | 13.32 | 20 | 24.57 | 19 | 19.56 | 18 | 29.85 | 11 | 12.11 | 14 | 19.74 | 11 | 2 | 00:00:02 | 8.94 |
Multiple Windows (5W) [7] | 16.88 | 7.09 | 15 | 19.52 | 16 | 12.56 | 19 | 25.43 | 20 | 20.72 | 19 | 33.10 | 19 | 12.33 | 15 | 21.27 | 12 | 2 | 00:00:02 | 9.44 |
Multiple Windows (25W) [7] | 17.13 | 7.40 | 16 | 18.09 | 13 | 14.72 | 22 | 22.66 | 16 | 21.01 | 20 | 32.74 | 17 | 12.96 | 18 | 22.12 | 15 | 8 | 00:00:14 | 12.56 |
Fixed Window | 17.50 | 6.94 | 14 | 28.26 | 26 | 7.47 | 14 | 37.65 | 25 | 16.81 | 11 | 36.66 | 22 | 8.79 | 10 | 22.95 | 18 | 1 | < 1 S | 9.25 |
Multiple Windows (9W) [7] | 17.50 | 7.80 | 18 | 18.68 | 14 | 14.74 | 23 | 23.98 | 17 | 21.06 | 21 | 32.88 | 18 | 12.52 | 16 | 21.51 | 13 | 4 | 00:00:05 | 10.75 |
Recursive Adaptive [3] | 18.50 | 9.90 | 23 | 25.40 | 23 | 10.76 | 17 | 16.50 | 12 | 19.26 | 17 | 32.14 | 15 | 13.67 | 20 | 25.68 | 21 | 14 | 00:17:01 | 16.25 |
Radial Adaptive [13] | 20.38 | 9.55 | 21 | 19.21 | 15 | 7.27 | 13 | 28.39 | 23 | 23.46 | 23 | 35.23 | 21 | 21.77 | 24 | 33.42 | 23 | 17 | 00:56:14 | 18.69 |
Multiple Adaptive [4] | 21.00 | 9.40 | 20 | 27.00 | 25 | 13.61 | 21 | 19.15 | 15 | 21.19 | 22 | 35.20 | 20 | 20.19 | 23 | 29.24 | 22 | 18 | 01:13:26 | 19.50 |
Max Connected [2] | 23.38 | 11.81 | 24 | 26.39 | 24 | 42.47 | 26 | 50.87 | 26 | 34.46 | 25 | 41.01 | 23 | 17.70 | 22 | 22.70 | 17 | 19 | 01:56:22 | 21.19 |
Oriented Rod [9] | 23.88 | 15.14 | 25 | 21.55 | 18 | 28.70 | 24 | 28.21 | 22 | 34.80 | 26 | 43.53 | 25 | 37.03 | 26 | 43.75 | 25 | 12 | 00:06:46 | 17.94 |
Oriented Rod* [9] | 23.88 | 17.21 | 26 | 22.75 | 22 | 29.26 | 25 | 26.57 | 21 | 32.70 | 24 | 41.16 | 24 | 32.51 | 25 | 40.04 | 24 | 13 | 00:06:48 | 18.44 |