Binary Stereo Matching

Average Error (%)
7
6.5
6
5.5
5
512 1024 2048 4096
Descriptor Length(bits)
(a) Quality
Average Running Time(s)
50
40
30
20
10
0
512 1024 2048 4096
Descriptor Length(bits)
(b) Speed
Figure 3. The influence of descriptor
length on the performance of BSM.
Also, the descriptor length affects depth map’s quality
because longer descriptor implies a dense sampling. To
show the influence of descriptor length on the performance
of BSM, we test BSM with different n on the
traditional four datasets from Middlebury. Experimental
result is shown in Figure 3, which is consistent with
the analysis above. This interesting property of BSM
makes it easy to gain different tradeoff between speed
and quality in different scenarios.
5. Conclusion
In this paper, a novel cost computation and aggregation
approach for stereo matching is proposed.
Combining our cost computation and aggregation approach
with WTA strategy, we design a new local stereo
method called binary stereo matching. The proposed algorithm
is mainly based on binary and integer computations,
so it is fast and fits for embedded or mobile devices.
Experimental results show that BSM has a better
performance either on traditional stereo datasets or on
new datasets with radiometric differences. In the future,
we will definitely incorporate our cost computation and
aggregation approach into global optimization and implement
BSM on GPU to achieve real-time matching.
6. Acknowledgement
This work has been partially supported by the
Development Plan of China (973) under Grant No.
2011CB302206, the National Natural Science Foundation
of China under Grant No. 60833009/60933013,
and the Research Grant of Tsinghua-Tencent Joint Lab.
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