Dense Stereomatching Algorithm Performance for View Prediction and Structure Reconstruction

Kostková, Jana; Čech, Jan; Šára, Radim

doi:10.1007/3-540-45103-x_14

Cited by 13 publications

(8 citation statements)

References 9 publications

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“…Taking the average of pixel errors at full image is not enough for performance evaluation [16]. [30] proposes region specific evaluations for areas of textureless, disparity discontinuities and occlusion.…”

Section: Robustness Evaluation For Stereo Visionmentioning

confidence: 99%

UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision

Zhang¹,

W²,

Chen³

et al. 2018

2018 International Conference on 3D Vision (3DV)

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A reliable stereo algorithm is critical for many robotics applications. But textureless and specular regions can easily cause failure by making feature matching difficult. Understanding whether an algorithm is robust to these hazardous regions is important. Although many stereo benchmarks have been developed to evaluate performance, it is hard to quantify the effect of hazardous regions in real images because the location and severity of these regions are unknown. In this paper, we develop a synthetic image generation tool enabling to control hazardous factors, such as making objects more specular or transparent, to produce hazardous regions at different degrees. The densely controlled sampling strategy in virtual worlds enables to effectively stress test stereo algorithms by varying the types and degrees of the hazard. We generate a large synthetic image dataset with automatically computed hazardous regions and analyze algorithms on these regions. The observations from synthetic images are further validated by annotating hazardous regions in real-world datasets Middlebury and KITTI (which gives a sparse sampling of the hazards). Our synthetic image generation tool is based on a game engine Unreal Engine 4 and will be open-source along with the virtual scenes in our experiments. Many publicly available realistic game contents can be used by our tool to provide an enormous resource for development and evaluation of algorithms.

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Section: Robustness Evaluation For Stereo Visionmentioning

confidence: 99%

UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision

Zhang¹,

W²,

Chen³

et al. 2018

2018 International Conference on 3D Vision (3DV)

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“…3 The above algorithm directly performs guiding or the least commitment strategy, as discussed in [24]: most reliable decisions are made prior to unreliable ones that wait until the set of putative solutions becomes more constrained. 4 The accuracy of stereoscopic matching based on the SSK is studied in [8].…”

Section: Matchingmentioning

confidence: 99%

Efficient Sampling of Disparity Space for Fast And Accurate Matching

Čech

Šára

2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

102

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A simple stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. Unlike in known seedgrowing algorithms, it guarantees matching accuracy and correctness, even in the presence of repetitive patterns. This success is based on the fact it solves a global optimization task. The algorithm can recover from wrong initial seeds to the extent they can even be random. The quality of correspondence seeds influences computing time, not the quality of the final disparity map. We show that the proposed algorithm achieves similar results as an exhaustive disparity space search but it is two orders of magnitude faster. This is very unlike the existing growing algorithms which are fast but erroneous. Accurate matching on 2-megapixel images of complex scenes is routinely obtained in a few seconds on a common PC from a small number of seeds, without limiting the disparity search range.1 The term disparity component has been coined in [1].

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“…If it does not, the corresponding part of the scene is rejected in the respective image pair. The CSM was used because is has a very low mismatch rate [7] and is fast. The single important parameter to CSM, for which a default value cannot be used, is the disparity search range.…”

Section: Radial Distortion Correctionmentioning

confidence: 99%

Towards Complete Free-Form Reconstruction of Complex 3D Scenes from an Unordered Set of Uncalibrated Images

Cornelius

Šára

Martinec

et al. 2004

Lecture Notes in Computer Science

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Abstract. This paper describes a method for accurate dense reconstruction of a complex scene from a small set of high-resolution unorganized still images taken by a hand-held digital camera. A fully automatic data processing pipeline is proposed. Highly discriminative features are first detected in all images. Correspondences are then found in all image pairs by wide-baseline stereo matching and used in a scene structure and camera reconstruction step that can cope with occlusion and outliers. Image pairs suitable for dense matching are automatically selected, rectified and used in dense binocular matching. The dense point cloud obtained as the union of all pairwise reconstructions is fused by local approximation using oriented geometric primitives. For texturing, every primitive is mapped on the image with the best resolution. The global structure reconstruction in the first step allows us to work with an unorganized set of images and to avoid error accumulation. By using object-centered geometric primitives we are able to preserve the flexibility of the method to describe complex free-form structures, preserve the possibility to build the dense model in an incremental way, and to retain the possibility to refine the cameras and the dense model by bundle adjustment. Results are demonstrated on partial models of a circular church and a Henri de Miller's sculpture. We observed spatial resolution in the range of centimeters on objects of about 20 m in size.

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Dense Stereomatching Algorithm Performance for View Prediction and Structure Reconstruction

Cited by 13 publications

References 9 publications

UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision

UnrealStereo: Controlling Hazardous Factors to Analyze Stereo Vision

Efficient Sampling of Disparity Space for Fast And Accurate Matching

Towards Complete Free-Form Reconstruction of Complex 3D Scenes from an Unordered Set of Uncalibrated Images

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