Accurate Multi-View Stereo 3D Reconstruction for Cost-Effective Plant Phenotyping

Lou, Lu; Liu, Yonghuai; Han, Jungong; Doonan, John H.

doi:10.1007/978-3-319-11755-3_39

Cited by 24 publications

(18 citation statements)

References 16 publications

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“…The most widespread Multi-View Stereo methods select for each camera a pairing view among the others by relying on several factors. Li et al [9] and Ebner et al [10] evaluate the baseline and the angle of the principal viewing direction between the cameras; instead, other methods [11,12,13,14] consider the SfM 3D points and take into account the average angle between the camera-to-point viewing rays, the baseline among views and the scale. Vogiatzis et al [15] leverage on similar metrics to filter out unreliable photometric measures, adopted to estimate the 3D model.…”

Section: Introductionmentioning

confidence: 99%

Mesh-based camera pairs selection and occlusion-aware masking for mesh refinement

Romanoni

Matteucci

2019

Pattern Recognition Letters

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Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space. Due to the limited scalability of such representations, the estimated model does not capture fine details of the scene. Therefore a mesh refinement algorithm is usually applied; it improves the mesh resolution and accuracy by minimizing the photometric error induced by the 3D model into pairs of cameras. The choice of these pairs significantly affects the quality of the refinement and usually relies on sparse 3D points belonging to the surface. Instead, in this paper, to increase the quality of pairs selection, we exploit the 3D model (before the refinement) to compute five metrics: scene coverage, mutual image overlap, image resolution, camera parallax, and a new symmetry term. To improve the refinement robustness, we also propose an explicit method to manage occlusions, which may negatively affect the computation of the photometric error. The proposed method takes into account the depth of the model while computing the similarity measure and its gradient. We quantitatively and qualitatively validated our approach on publicly available datasets against state of the art reconstruction methods.

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Section: Introductionmentioning

confidence: 99%

Mesh-based camera pairs selection and occlusion-aware masking for mesh refinement

Romanoni

Matteucci

2019

Pattern Recognition Letters

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“…The same imaging system was adopted on the Robotanist (Mueller‐Sim et al, ) for sorghum stem count and stem width estimation (Baweja, Parhar, Mirbod, & Nuske, ). In controlled environments, multiview stereo can provide a detailed 3D reconstruction at a low cost (Lou, Liu, Han, & Doonan, ; Nguyen, Slaughter, Maloof, & Sinha, ). Structured‐light pattern illumination was used to add texture on the textureless plant surfaces to facilitate stereo matching (Nguyen, Slaughter, Max, Maloof, & Sinha, ).…”

Section: Introductionmentioning

confidence: 99%

Field‐based robotic phenotyping of sorghum plant architecture using stereo vision

Bao

Tang

Breitzman

et al. 2018

Journal of Field Robotics

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Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time-consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high-throughput field-based robotic phenotyping system which performed side-view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field-based three-dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated.A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot-based plant height, plot-based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image-derived measurements were highly repeatable and showed high correlations with the in-field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large-scale field-based high-throughput plant phenotyping of bioenergy crops. K E Y W O R D Sbioenergy sorghum, high-throughput phenotyping, point cloud processing, stereo vision, yield component traits

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“…In recent years, the rapid improvement of plant phenotyping processes and the use of automated systems are providing valuable tools for comprehensive characterization of plant traits [ 4 ]. Many authors have emphasized the importance of accurate and efficient methods in order to obtain proper plant phenotyping [ 5 , 6 ]. The importance of a good model leads to a good understanding of the architecture of plants and how they intercept light for photosynthesis processes.…”

Section: Introductionmentioning

confidence: 99%

Matching the Best Viewing Angle in Depth Cameras for Biomass Estimation Based on Poplar Seedling Geometry

Andújar

Fernández‐Quintanilla

Dorado

2015

Sensors

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In energy crops for biomass production a proper plant structure is important to optimize wood yields. A precise crop characterization in early stages may contribute to the choice of proper cropping techniques. This study assesses the potential of the Microsoft Kinect for Windows v.1 sensor to determine the best viewing angle of the sensor to estimate the plant biomass based on poplar seedling geometry. Kinect Fusion algorithms were used to generate a 3D point cloud from the depth video stream. The sensor was mounted in different positions facing the tree in order to obtain depth (RGB-D) images from different angles. Individuals of two different ages, e.g., one month and one year old, were scanned. Four different viewing angles were compared: top view (0°), 45° downwards view, front view (90°) and ground upwards view (−45°). The ground-truth used to validate the sensor readings consisted of a destructive sampling in which the height, leaf area and biomass (dry weight basis) were measured in each individual plant. The depth image models agreed well with 45°, 90° and −45° measurements in one-year poplar trees. Good correlations (0.88 to 0.92) between dry biomass and the area measured with the Kinect were found. In addition, plant height was accurately estimated with a few centimeters error. The comparison between different viewing angles revealed that top views showed poorer results due to the fact the top leaves occluded the rest of the tree. However, the other views led to good results. Conversely, small poplars showed better correlations with actual parameters from the top view (0°). Therefore, although the Microsoft Kinect for Windows v.1 sensor provides good opportunities for biomass estimation, the viewing angle must be chosen taking into account the developmental stage of the crop and the desired parameters. The results of this study indicate that Kinect is a promising tool for a rapid canopy characterization, i.e., for estimating crop biomass production, with several important advantages: low cost, low power needs and a high frame rate (frames per second) when dynamic measurements are required.

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Accurate Multi-View Stereo 3D Reconstruction for Cost-Effective Plant Phenotyping

Abstract: Sponsorship: BBSRC RONO: BBS/E/W/10961A0

Cited by 24 publications

References 16 publications

Mesh-based camera pairs selection and occlusion-aware masking for mesh refinement

Mesh-based camera pairs selection and occlusion-aware masking for mesh refinement

Field‐based robotic phenotyping of sorghum plant architecture using stereo vision

Matching the Best Viewing Angle in Depth Cameras for Biomass Estimation Based on Poplar Seedling Geometry

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