An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images

Haq, Qazi Mazhar ul; Lin, Chang Hong; Ruan, Shanq-Jang; Gregor, Derlis

doi:10.1007/s12652-021-02958-8

Cited by 15 publications

(7 citation statements)

References 41 publications

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“…As shown in Figure 2, 3D reconstruction with stereo vision was achieved through steps such as stereo matching, depth value calculation, triangulation, and texture mapping. Stereo matching refers to establishing the corresponding relation between a pair of images according to the extracted features; that is, mapping the same physical space points in two different images one by one [43][44][45]. Image preprocessing is required before stereo matching [46].…”

Section: Stereo Reconstruction Algorithm For Unstructured Scenesmentioning

confidence: 99%

“…Phase matching is based on the assumption that the local phases between the corresponding pixels of two corresponding images should be equal and has a low bit error rate, but phase deviation has a huge impact on the matching accuracy. Feature Stereo matching refers to establishing the corresponding relation between a pair of images according to the extracted features; that is, mapping the same physical space points in two different images one by one [43][44][45]. Image preprocessing is required before stereo matching [46].…”

Section: Sad-fast Feature Detection and Recombination Stereo-matching...mentioning

confidence: 99%

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Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision

et al. 2023

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At present, Chinese 3D reconstruction solutions using stereo cameras mainly face known, indoor, structured scenes; for the reconstruction of unstructured, larger-scale scenes with a large variety of texture information of different intensities, there are certain difficulties in ensuring accuracy and real-time processing. For the above problems, we propose a scene reconstruction method using stereo vision. Firstly, considering the influence of outdoor lighting and weather on the captured 2D images, the optimized SAD-FAST feature detection algorithm and stereo-matching strategy were employed in the stereo-matching stage to improve the overall efficiency and matching quality at this stage. Then, a homogenized feature extraction algorithm with gradient value decreasing step by step (GVDS) was used in the depth value calculation to ensure a sufficient number of feature points for strong texture information while extracting features from weak-texture areas, which greatly improved the quality and speed of unstructured scene reconstruction. We conducted experiments to validate the proposed method, and the results showed the feasibility of the proposed method and its high practical value.

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Section: Stereo Reconstruction Algorithm For Unstructured Scenesmentioning

confidence: 99%

Section: Sad-fast Feature Detection and Recombination Stereo-matching...mentioning

confidence: 99%

Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision

et al. 2023

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“…As a result of executing the reduction algorithm, the number of points representing the background and the number of points inside the object were reduced. Compared to the original point cloud, the reduced point cloud output the edge data of the object [24]. The reduced data of one LiDAR sensor, as shown in Figure 12c had sufficient data to distinguish the human shape; however, the reconstruction accuracy was reduced because it had less information compared to the original data.…”

Section: Reductionmentioning

confidence: 99%

Semantic Depth Data Transmission Reduction Techniques Based on Interpolated 3D Plane Reconstruction for Light-Weighted LiDAR Signal Processing Platform

2022

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In vehicles for autonomous driving, light detection and ranging (LiDAR) is one of the most used sensors, along with cameras. LiDAR sensors that produce a large amount of data in one scan make it difficult to transmit and calculate data in real-time in the vehicle’s embedded system. In this paper, we propose a platform based on semantic depth data-based data reduction and reconstruction algorithms that reduce the amount of data transmission and minimize the errors between original and restored data in a vehicle system using four LiDAR sensors. The proposed platform consists of four LiDAR sensors, an integrated processing unit (IPU) that reduces the data of the LiDAR sensors, and the main processor that reconstructs the reduced data and processes the image. In the proposed platform, the 58,000 bytes of data constituting one frame detected by the VL-AS16 LiDAR sensor were reduced by an average of 87.4% to 7295 bytes by the data reduction algorithm. In the IPU placed near the LiDAR sensor, the memory usage increased by the data reduction algorithm, but the data transmission time decreased by an average of 40.3%. The transmission time where the vehicle’s processor received one frame of data decreased from an average of 1.79 to 0.28 . Compared with the original LiDAR sensor data, the reconstructed data showed an average error of 4.39% in the region of interest (ROI). The proposed platform increased the time required for image processing in the vehicle’s main processor by an average of 6.73% but reduced the amount of data by 87.4% with a decrease in data accuracy of 4.39%.

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“…The intensities of very few pixels are involved in evaluating an initial matching cost [4]. Then, support is aggregated given the initial costs of neighboring pixels believed to belong to the same depth [5]. Intensity-based metrics are fast to be computed.…”

Section: International Journal Of Intelligent Computing and Information Sciencesmentioning

confidence: 99%

“…[7]. Dense matching can be performed by extracting a descriptor for each pixel [8] and comparing these robust descriptors to calculate the matching cost as illustrated by descriptorbased stereo matching [5], [9], SIFT-flow [10], binary stereo matching [11], DAISY [12], [13]. Distribution-based descriptors , such as SIFT [6] and SURF (Speeded Up Robust Features [14]), have high computational complexity [15].…”

Section: International Journal Of Intelligent Computing and Information Sciencesmentioning

confidence: 99%

Binary Descriptors for Dense Stereo Matching

Ibrahim

Khaled

Seada

et al. 2021

IJICIS

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Dense local stereo matching is traditionally based on initial cost evaluation using a simple metric followed by sophisticated support aggregation. There is a high potential of replacing these simple metrics by robust binary descriptors. However, the available studies focus on comparing descriptors for sparse matching rather than the dense case of extracting a descriptor per each pixel. Therefore, this paper studies the design decisions of well-established binary descriptors such as BRIEF (Binary Robust Independent Elementary Features), ORB (Oriented FAST and rotated BRIEF), BRISK (Binary Robust Invariant Scalable Keypoints) and FREAK (Fast Retina Keypoint) to decide their suitablility for the dense matching case. The results shows that support agregation is required for use with binary descriptors to handle edges in local dense matching. Also, BRIEF produced the smoothnest disparity map if geometric transformations is not present. Whereas, FREAK and BRISK achieved the least overall error percentage across all regions. The lastest Middlebury Stereo benchmark is utilized in the experiments.

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An edge-aware based adaptive multi-feature set extraction for stereo matching of binocular images

Cited by 15 publications

References 41 publications

Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision

Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision

Semantic Depth Data Transmission Reduction Techniques Based on Interpolated 3D Plane Reconstruction for Light-Weighted LiDAR Signal Processing Platform

Binary Descriptors for Dense Stereo Matching

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