A Multiview Stereo Algorithm Based on Image Segmentation Guided Generation of Planar Prior for Textureless Regions of Artificial Scenes

Huang, Nan; Huang, Zijie; Fu, Chuanyu; Zhou, Hewen; Xia, Yimin; Li, Weijun; Xiong, Xiaoming; Cai, Shuting

doi:10.1109/jstars.2023.3237588

Cited by 3 publications

(3 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [20] proposes a plane prior generation method by combining meanshift clustering and superpixel segmentation, then introduces planar priors and smooth constraints into the cost. The image gradient is used to adaptively adjust the weights of different constraints in the cost.…”

Section: Related Workmentioning

confidence: 99%

“…In structured scenes, surfaces with weakly textured regions can be approximately characterized as identical planes. This allows the plane-based methodology [16][17][18][19][20][21] to effectively guide the elimination of the fuzzy matching problem that occurs in weakly textured regions, then improves the completeness of the reconstruction. Following their previous work, the authors of [18,22] introduce the prior plane to help the recovery of weakly textured regions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Confidence-Guided Planar-Recovering Multiview Stereo for Weakly Textured Plane of High-Resolution Image Scenes

Huang

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Multiview stereo (MVS) achieves efficient 3D reconstruction on Lambertian surfaces and strongly textured regions. However, the reconstruction of weakly textured regions, especially planar surfaces in weakly textured regions, still faces significant challenges due to the fuzzy matching problem of photometric consistency. In this paper, we propose a multiview stereo for recovering planar surfaces guided by confidence calculations, resulting in the construction of large-scale 3D models for high-resolution image scenes. Specifically, a confidence calculation method is proposed to express the reliability degree of plane hypothesis. It consists of multiview consistency and patch consistency, which characterize global contextual information and local spatial variation, respectively. Based on the confidence of plane hypothesis, the proposed plane supplementation generates new reliable plane hypotheses. The new planes are embedded in the confidence-driven depth estimation. In addition, an adaptive depth fusion approach is proposed to allow regions with insufficient visibility to be effectively fused into the dense point clouds. The experimental results illustrate that the proposed method can lead to a 3D model with competitive completeness and high accuracy compared with state-of-the-art methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Confidence-Guided Planar-Recovering Multiview Stereo for Weakly Textured Plane of High-Resolution Image Scenes

Huang

et al. 2023

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…The first deep learning-based 3D reconstruction network in this field, MVSNet, was proposed by Yao et al [19] but suffers from the problem of poor reconstruction accuracy. Aiming at the problem that the MVSNet model using recurrent neural network does not effectively consider the contextual information, Yan et al [20] proposed the D2HC-RMVSNet architecture, where existing 3D reconstruction methods expand the receptive field in the feature extraction part by downsampling, which causes feature loss and thus affects the reconstruction completeness, and cited a new feature extraction structure, DRE-Net, which enlarges the receptive field by inflated convolution and no longer performs downsampling [21,22]. Overall, MVS inherits the stereo geometry theoretical basis of stereo matching, and with the help of more image viewpoints, it effectively improves the influence of the occlusion problem and achieves a big improvement in both accuracy and generalization.…”

Section: Introductionmentioning

confidence: 99%

Multi-View Jujube Tree Trunks Stereo Reconstruction Based on UAV Remote Sensing Imaging Acquisition System

Ling,

Li,

Ding

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme.

show abstract

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data

Li,

Xue,

et al. 2024

Foods

View full text Add to dashboard Cite

Quality management in the candy industry is a vital part of food quality management. Defective candies significantly affect subsequent packaging and consumption, impacting the efficiency of candy manufacturers and the consumer experience. However, challenges exist in candy defect detection on food production lines due to the small size of the targets and defects, as well as the difficulty of batch sampling defects from automated production lines. A high-precision candy defect detection method based on deep learning is proposed in this paper. Initially, pseudo-defective candy images are generated based on Style Generative Adversarial Network-v2 (StyleGAN2), thereby enhancing the authenticity of these synthetic defect images. Following the separation of the background based on the color characteristics of the defective candies on the conveyor belt, a GAN is utilized for negative sample data enhancement. This effectively reduces the impact of data imbalance between complete and defective candies on the model’s detection performance. Secondly, considering the challenges brought by the small size and random shape of candy defects to target detection, the efficient target detection method YOLOv7 is improved. The Spatial Pyramid Pooling Fast Cross Stage Partial Connection (SPPFCSPC) module, the C3C2 module, and the global attention mechanism are introduced to enhance feature extraction precision. The improved model achieves a 3.0% increase in recognition accuracy and a 3.7% increase in recall rate while supporting real-time recognition scenery. This method not only enhances the efficiency of food quality management but also promotes the application of computer vision and deep learning in industrial production.

show abstract

A Multiview Stereo Algorithm Based on Image Segmentation Guided Generation of Planar Prior for Textureless Regions of Artificial Scenes

Cited by 3 publications

References 53 publications

Confidence-Guided Planar-Recovering Multiview Stereo for Weakly Textured Plane of High-Resolution Image Scenes

Confidence-Guided Planar-Recovering Multiview Stereo for Weakly Textured Plane of High-Resolution Image Scenes

Multi-View Jujube Tree Trunks Stereo Reconstruction Based on UAV Remote Sensing Imaging Acquisition System

A Candy Defect Detection Method Based on StyleGAN2 and Improved YOLOv7 for Imbalanced Data

Contact Info

Product

Resources

About