Stereo Classification and Performance Evaluation

cvlab
|SAD|TAD|SSD|original|

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 Sum of Absolute Differences (SAD) 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:

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
Segment support [10] 1.00 2.28 1 7.50 1 1.21 1 5.88 1 10.99 1 22.01 1 5.42 1 11.83 1 17 00:33:34 9.00
Adaptive weight [14] 2.50 4.66 3 8.25 2 4.61 3 13.30 4 12.70 2 22.40 2 5.50 2 11.90 2 15 00:18:14 8.75
Variable Windows [12] 4.00 4.10 2 10.79 3 10.66 13 9.94 2 13.93 3 25.53 3 7.24 3 13.86 3 11 00:00:25 7.50
Reliability [8] 5.38 5.14 4 18.31 5 3.86 2 11.51 3 16.96 6 30.62 6 13.52 13 21.55 4 16 00:21:51 10.69
Shiftable Windows [11] 5.63 6.53 7 21.80 8 6.60 5 13.54 5 16.16 5 30.19 5 9.55 4 22.99 6 7 00:00:15 6.31
Segmentation based [5] 7.30 8.18 10 18.77 6 8.06 8 20.85 7 15.78 4 29.66 4 13.22 12 24.55 8 2 00:00:02 4.69
Multiple Windows (25W)* [7] 8.13 6.52 6 21.91 9 6.77 6 21.57 8 18.60 10 33.11 9 11.87 10 23.69 7 9 00:00:16 8.56
Recursive Adaptive [3] 10.00 9.22 14 26.69 16 8.36 9 14.86 6 18.48 9 32.93 8 11.60 9 24.80 9 14 00:16:55 12.00
Gradient Guided [6] 10.50 7.51 9 16.20 4 13.07 14 33.10 13 20.39 13 32.82 7 13.67 14 25.26 10 3 00:00:03 6.75
Multiple Windows (9W)* [7] 10.75 8.51 11 27.59 17 6.47 4 34.30 15 17.57 8 38.04 13 10.75 6 26.60 12 6 00:00:13 8.38
Multiple Windows (5W)* [7] 11.25 9.36 15 25.74 14 8.57 10 38.65 17 17.11 7 37.45 11 9.86 5 25.33 11 8 00:00:16 9.63
Multiple Windows (25W) [7] 11.38 6.34 5 24.13 11 9.04 11 29.61 10 20.77 14 36.77 10 14.20 15 27.45 15 10 00:00:17 10.69
Multiple Windows (9W) [7] 11.50 7.12 8 25.00 13 10.21 12 33.44 14 18.91 12 37.76 12 10.95 7 27.05 14 5 00:00:09 8.25
Multiple Windows (5W) [7] 13.00 8.94 13 23.55 10 16.33 15 35.56 16 22.29 15 38.09 14 11.13 8 26.99 13 4 00:00:07 8.50
Fixed Window 14.25 8.66 12 36.67 20 7.05 7 40.53 19 18.68 11 41.95 17 12.79 11 33.32 17 1 < 1 S 7.63
Max Connected [2] 15.88 11.81 17 26.39 15 42.47 20 50.87 20 34.46 18 41.01 16 17.70 16 22.70 5 20 02:00:55 17.94
Multiple Adaptive [4] 15.88 15.11 19 32.85 19 16.88 16 22.31 9 25.40 16 39.44 15 21.40 17 29.66 16 18 00:39:47 16.94
Oriented Rod [9] 16.63 11.29 16 24.21 12 26.33 18 30.09 11 37.68 19 47.94 19 39.60 19 47.88 19 12 00:12:22 14.31
Oriented Rod* [9] 17.50 15.87 20 27.75 18 26.40 19 30.58 12 30.65 17 42.72 18 30.52 18 41.82 18 13 00:12:33 15.25
Radial Adaptive [13] 17.50 14.84 18 21.79 7 22.40 17 40.40 18 49.64 20 50.13 20 50.18 20 53.60 20 19 00:58:52 18.25

References

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