A Practical 3D Reconstruction Method for Weak Texture Scenes

Yang, Xuyuan; Jiang, Guang

doi:10.3390/rs13163103

Cited by 16 publications

(3 citation statements)

References 44 publications

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“…Clearly, image-based underwater 3D reconstruction is extremely cost-effective [194]. It is inexpensive, simple and quick, while providing essential visual information.…”

Section: Discussionmentioning

confidence: 99%

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

Wang

Shen

et al. 2023

JMSE

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At present, 3D reconstruction technology is being gradually applied to underwater scenes and has become a hot research direction that is vital to human ocean exploration and development. Due to the rapid development of computer vision in recent years, optical image 3D reconstruction has become the mainstream method. Therefore, this paper focuses on optical image 3D reconstruction methods in the underwater environment. However, due to the wide application of sonar in underwater 3D reconstruction, this paper also introduces and summarizes the underwater 3D reconstruction based on acoustic image and optical–acoustic image fusion methods. First, this paper uses the Citespace software to visually analyze the existing literature of underwater images and intuitively analyze the hotspots and key research directions in this field. Second, the particularity of underwater environments compared with conventional systems is introduced. Two scientific problems are emphasized by engineering problems encountered in optical image reconstruction: underwater image degradation and the calibration of underwater cameras. Then, in the main part of this paper, we focus on the underwater 3D reconstruction methods based on optical images, acoustic images and optical–acoustic image fusion, reviewing the literature and classifying the existing solutions. Finally, potential advancements in this field in the future are considered.

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“…Clearly, image-based underwater 3D reconstruction is extremely cost-effective [194]. It is inexpensive, simple and quick, while providing essential visual information.…”

Section: Discussionmentioning

confidence: 99%

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

Wang

Shen

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…For example, Yang et al [41] use a light-weight depth refinement network to improve the noisy depths of textureless regions produced by multi-view semi-global matching (SGM). Yang and Jiang [42] combine deep learning algorithms with traditional methods to extract and match feature points from light pattern augmented images to improve a practical 3D reconstruction method for weakly textured scenes. Stathopoulou et al [43] tackle the textureless problem by leveraging semantic priors into a PatchMatch-based MVS in order to increase confidence and better support depth and normal map estimation on weakly textured areas.…”

Section: Depth-map Merging Based Methodsmentioning

confidence: 99%

DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes

et al. 2021

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This paper presents an accurate and robust dense 3D reconstruction system for detail preserving surface modeling of large-scale scenes from multi-view images, which we named DP-MVS. Our system performs high-quality large-scale dense reconstruction, which preserves geometric details for thin structures, especially for linear objects. Our framework begins with a sparse reconstruction carried out by an incremental Structure-from-Motion. Based on the reconstructed sparse map, a novel detail preserving PatchMatch approach is applied for depth estimation of each image view. The estimated depth maps of multiple views are then fused to a dense point cloud in a memory-efficient way, followed by a detail-aware surface meshing method to extract the final surface mesh of the captured scene. Experiments on ETH3D benchmark show that the proposed method outperforms other state-of-the-art methods on F1-score, with the running time more than 4 times faster. More experiments on large-scale photo collections demonstrate the effectiveness of the proposed framework for large-scale scene reconstruction in terms of accuracy, completeness, memory saving, and time efficiency.

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“…Liang et al [30] established a two-prism stereo-vision system and proposed a two-step stereo-matching algorithm to reconstruct the surface 3D shape, but since some areas on reconstructed surfaces are discontinuous, it cannot be applied to our reconstruction of unstructured scenes. Considering the popularity of deep learning in recent years, Yang and Jiang [31] combined deep learning algorithms with traditional algorithms to extract and match feature points from optical pattern-enhanced images to improve practical 3D reconstruction methods for weakly textured scenes. Stathopoulou et al [32] solved the texture-free problem by exploiting semantic priors for the PatchMatch-based MVS to increase confidence and better support depth and normal mapping estimation in weakly textured regions.…”

Section: Introductionmentioning

confidence: 99%

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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A Practical 3D Reconstruction Method for Weak Texture Scenes

Cited by 16 publications

References 44 publications

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

Overview of Underwater 3D Reconstruction Technology Based on Optical Images

DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes

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

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