Ensemble Learning for Confidence Measures in Stereo Vision
With the aim to improve accuracy of stereo confidence measures, we apply the random decision forest framework to a large set of diverse stereo confidence measures. Learning and testing sets were drawn from the recently introduced KITTI dataset, which currently poses higher challenges to stereo solvers than other benchmarks with ground truth for stereo evaluation. We experiment with semi global matching stereo (SGM) and a census dataterm, which is the best performing realtime capable stereo method known to date. On KITTI images, SGM still produces a significant amount of error. We obtain consistently improved area under curve values of sparsification measures in comparison to best performing single stereo confidence measures where numbers of stereo errors are large. More specifically, our method performs best in all but one out of 194 frames of the KITTI dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Ensemble LearningSimilar Papers 제목 키워드 기반
Using Self-Contradiction to Learn Confidence Measures in Stereo Vision
Learned confidence measures gain increasing importance for outlier removal and quality improvement in stereo vision. However, acquiring the necessary training data is typically a tedious and time consuming task that invo…
Leveraging Stereo Matching With Learning-Based Confidence Measures
We propose a new approach to associate supervised learning-based confidence prediction with the stereo matching problem. First of all, we analyze the characteristics of various confidence measures in the regression fores…
regressionStereo MatchingStereo Matching HandQuantitative Evaluation of Confidence Measures in a Machine Learning World
Confidence measures aim at detecting unreliable depth measurements and play an important role for many purposes and in particular, as recently shown, to improve stereo accuracy. This topic has been thoroughly investigate…
BIG-bench Machine LearningLearning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps
Confidence measures estimate unreliable disparity assignments performed by a stereo matching algorithm and, as recently proved, can be used for several purposes. This paper aims at increasing, by means of a deep network,…
Stereo MatchingStereo Matching HandRobust Confidence Intervals in Stereo Matching using Possibility Theory
We propose a method for estimating disparity confidence intervals in stereo matching problems. Confidence intervals provide complementary information to usual confidence measures. To the best of our knowledge, this is th…
Stereo Matching