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Table 1 A comparison of CUDA-based stereo matching algorithms on Middlebury and KITTI datasets

From: Real-time CUDA-based stereo matching using Cyclops2 algorithm

Method Middlebury (1436 × 992 single image) KITTI (1242 × 375 single image) Advantages/disadvantages
Avg. runtime, s Bad pixels (error > 4 pixels), % Avg. runtime, s Bad pixels (error > 3 pixels), %  
BM [5] x x 0.1 25.27 Sparse results, high error rates, does not work in untextured areas, CPU implementation
SGM [6] 9.9 12.2 x x Non optimal matching cost, performance depends on parameter choice, CPU implementation
SGBM [8] 2.27 18.5 1.1 10.86 Simple matching cost, fast computation time even with CPU implementation
CSCT [10] x x 0.0064 8.24 Simple GPU model, hard to adjust to complex tasks
IDR [11] 0.49 12.7 x x Strict binary weights near boundaries
MC-CNN-ACRT [12] 150 4.48 67 3.89 Complexity of training CNN, slow speed
MC-CNN-FST [12] 1.69 6.7 x x CNN optimised for speed, requires powerful GPU
C-CNN [13] x x 1.00 4.54 Smoothing technology dependent
DNET [14] x x 0.06 4.34 Dataset specific, added complexity of CNN