Image Redundancy Research Articles

Asymmetric patch sampling for contrastive learning

Asymmetric appearance between positive pair effectively reduces the risk of representation degradation in contrastive learning. However, there are still a mass of appearance similarities between positive pair constructed by the existing methods, thus inhibiting the further representation improvement. To address the above issue, we propose a novel asymmetric patch sampling strategy, which significantly reduces the appearance similarities but retains the image semantics. Specifically, dual patch sampling strategies are respectively applied to the given image. First, sparse patch sampling is conducted to obtain the first view, which reduces spatial redundancy of image and allows a more asymmetric view. Second, a selective patch sampling is proposed to construct another view with large appearance discrepancy relative to the first one. Due to the inappreciable appearance similarities between positive pair, the trained model is encouraged to capture the similarities on semantics, instead of low-level ones.Experimental results demonstrate that our method significantly outperforms the existing self-supervised learning methods on ImageNet-1K and CIFAR datasets, e.g., 2.5% finetuning accuracy improvement on CIFAR100. Furthermore, our method achieves state-of-the-art performance on downstream tasks, object detection and instance segmentation on COCO. Additionally, compared to other self-supervised methods, our method is more efficient on both memory and computation during pretraining. The source code and the trained weights are available at https://github.com/visresearch/aps.

Appropriateness of Imaging for Low-Risk Prostate Cancer-Real World Data from the Pennsylvania Urologic Regional Collaboration (PURC).

Imaging for prostate cancer defines the extent of disease. Guidelines recommend against imaging low-risk prostate cancer patients with a computed tomography (CT) scan or bone scan due to the low probability of metastasis. We reviewed imaging performed for men diagnosed with low-risk prostate cancer across the Pennsylvania Urologic Regional Collaborative (PURC), a physician-led data sharing and quality improvement collaborative. The data of 10 practices were queried regarding the imaging performed in men diagnosed with prostate cancer from 2015 to 2022. The cohort included 13,122 patients with 3502 (27%) low-risk, 2364 (18%) favorable intermediate-risk, 3585 (27%) unfavorable intermediate-risk, and 3671 (28%) high-risk prostate cancer, based on the AUA guidelines. Amongst the low-risk patients, imaging utilization included pelvic MRI (59.7%), bone scan (17.8%), CT (16.0%), and PET-based imaging (0.5%). Redundant imaging occurred in 1022 patients (29.2%). There was variability among the PURC sites for imaging used in the low-risk patients, and iterative education reduced the need for CT and bone scans. Approximately 15% of low-risk patients had staging imaging performed using either a CT or bone scan, and redundant imaging occurred in almost one-third of men. Such data underscore the need for continued guideline-based education to optimize the stewardship of resources and reduce unnecessary costs to the healthcare system.

Enhancing geotechnical damage detection with deep learning: a convolutional neural network approach.

Most natural disasters result from geodynamic events such as landslides and slope collapse. These failures cause catastrophes that directly impact the environment and cause financial and human losses. Visual inspection is the primary method for detecting failures in geotechnical structures, but on-site visits can be risky due to unstable soil. In addition, the body design and hostile and remote installation conditions make monitoring these structures inviable. When a fast and secure evaluation is required, analysis by computational methods becomes feasible. In this study, a convolutional neural network (CNN) approach to computer vision is applied to identify defects in the surface of geotechnical structures aided by unmanned aerial vehicle (UAV) and mobile devices, aiming to reduce the reliance on human-led on-site inspections. However, studies in computer vision algorithms still need to be explored in this field due to particularities of geotechnical engineering, such as limited public datasets and redundant images. Thus, this study obtained images of surface failure indicators from slopes near a Brazilian national road, assisted by UAV and mobile devices. We then proposed a custom CNN and low complexity model architecture to build a binary classifier image-aided to detect faults in geotechnical surfaces. The model achieved a satisfactory average accuracy rate of 94.26%. An AUC metric score of 0.99 from the receiver operator characteristic (ROC) curve and matrix confusion with a testing dataset show satisfactory results. The results suggest that the capability of the model to distinguish between the classes 'damage' and 'intact' is excellent. It enables the identification of failure indicators. Early failure indicator detection on the surface of slopes can facilitate proper maintenance and alarms and prevent disasters, as the integrity of the soil directly affects the structures built around and above it.

Automatic localization of image semantic patches for crop disease recognition

Crop disease recognition plays a crucial role in agricultural production. However, disease images are large in scale and have a lot of redundant information, which reduces the effectiveness of deep neural networks in extracting diseases. To address the above issues and considering that not all image regions are relevant to disease recognition, this study proposes an efficient crop disease recognition method with dynamic reduction of image redundancy. The method is a two-stage process. In the first stage, we employ the lightweight CA-AnchorNet, which incorporates coordinate attention, to swiftly generate a feature map of the affected crop areas. Subsequently, class activation maps (CAMs) are utilized to identify the disease feature regions, highlighting areas that exhibit class discriminability. These regions are then mapped to a higher resolution from the original lower-resolution image, and the target patch is extracted. In the second stage, these local semantic patches, characterized by reduced spatial redundancy, are fed into the lightweight PatchNet for accurate recognition. PatchNet incorporates the Inception-C module and the ACON-C activation function. These features enhance the model's ability to express multi-scale and non-linear characteristics of crop disease features. This method does not require manually annotated region boxes and achieves an identification accuracy of 99.86 % on a 12-class crop disease dataset with complex environments. The parameter count is only 0.98 M. This method has the characteristics of accurate localization and low parameter count, and can be used for effective and high-precision recognition of crop diseases in complex environments.

Self-Supervised Medical Image Segmentation Using Deep Reinforced Adaptive Masking.

Self-supervised learning aims to learn transferable representations from unlabeled data for downstream tasks. Inspired by masked language modeling in natural language processing, masked image modeling (MIM) has achieved certain success in the field of computer vision, but its effectiveness in medical images remains unsatisfactory. This is mainly due to the high redundancy and small discriminative regions in medical images compared to natural images. Therefore, this paper proposes an adaptive hard masking (AHM) approach based on deep reinforcement learning to expand the application of MIM in medical images. Unlike predefined random masks, AHM uses an asynchronous advantage actor-critic (A3C) model to predict reconstruction loss for each patch, enabling the model to learn where masking is valuable. By optimizing the non-differentiable sampling process using reinforcement learning, AHM enhances the understanding of key regions, thereby improving downstream task performance. Experimental results on two medical image datasets demonstrate that AHM outperforms state-of-the-art methods. Additional experiments under various settings validate the effectiveness of AHM in constructing masked images.

ODNet: A High Real-Time Network Using Orthogonal Decomposition for Few-Shot Strip Steel Surface Defect Classification.

Strip steel plays a crucial role in modern industrial production, where enhancing the accuracy and real-time capabilities of surface defect classification is essential. However, acquiring and annotating defect samples for training deep learning models are challenging, further complicated by the presence of redundant information in these samples. These issues hinder the classification of strip steel surface defects. To address these challenges, this paper introduces a high real-time network, ODNet (Orthogonal Decomposition Network), designed for few-shot strip steel surface defect classification. ODNet utilizes ResNet as its backbone and incorporates orthogonal decomposition technology to reduce the feature redundancies. Furthermore, it integrates skip connection to preserve essential correlation information in the samples, preventing excessive elimination. The model optimizes the parameter efficiency by employing Euclidean distance as the classifier. The orthogonal decomposition not only helps reduce redundant image information but also ensures compatibility with the Euclidean distance requirement for orthogonal input. Extensive experiments conducted on the FSC-20 benchmark demonstrate that ODNet achieves superior real-time performance, accuracy, and generalization compared to alternative methods, effectively addressing the challenges of few-shot strip steel surface defect classification.

Mangrove Species Classification from Unmanned Aerial Vehicle Hyperspectral Images Using Object-Oriented Methods Based on Feature Combination and Optimization.

Accurate and timely acquisition of the spatial distribution of mangrove species is essential for conserving ecological diversity. Hyperspectral imaging sensors are recognized as effective tools for monitoring mangroves. However, the spatial complexity of mangrove forests and the spectral redundancy of hyperspectral images pose challenges to fine classification. Moreover, finely classifying mangrove species using only spectral information is difficult due to spectral similarities among species. To address these issues, this study proposes an object-oriented multi-feature combination method for fine classification. Specifically, hyperspectral images were segmented using multi-scale segmentation techniques to obtain different species of objects. Then, a variety of features were extracted, including spectral, vegetation indices, fractional order differential, texture, and geometric features, and a genetic algorithm was used for feature selection. Additionally, ten feature combination schemes were designed to compare the effects on mangrove species classification. In terms of classification algorithms, the classification capabilities of four machine learning classifiers were evaluated, including K-nearest neighbor (KNN), support vector machines (SVM), random forests (RF), and artificial neural networks (ANN) methods. The results indicate that SVM based on texture features achieved the highest classification accuracy among single-feature variables, with an overall accuracy of 97.04%. Among feature combination variables, ANN based on raw spectra, first-order differential spectra, texture features, vegetation indices, and geometric features achieved the highest classification accuracy, with an overall accuracy of 98.03%. Texture features and fractional order differentiation are identified as important variables, while vegetation index and geometric features can further improve classification accuracy. Object-based classification, compared to pixel-based classification, can avoid the salt-and-pepper phenomenon and significantly enhance the accuracy and efficiency of mangrove species classification. Overall, the multi-feature combination method and object-based classification strategy proposed in this study provide strong technical support for the fine classification of mangrove species and are expected to play an important role in mangrove restoration and management.

Depth Supervised Neural Surface Reconstruction from Airborne Imagery

Abstract. While originally developed for novel view synthesis, Neural Radiance Fields (NeRFs) have recently emerged as an alternative to multi-view stereo (MVS). Triggered by a manifold of research activities, promising results have been gained especially for textureless, transparent, and reflecting surfaces, while such scenarios remain challenging for traditional MVS-based approaches. However, most of these investigations focus on close-range scenarios, with studies for airborne scenarios still missing. For this task, NeRFs face potential difficulties at areas of low image redundancy and weak data evidence, as often found in street canyons, facades or building shadows. Furthermore, training such networks is computationally expensive. Thus, the aim of our work is twofold: First, we investigate the applicability of NeRFs for aerial image blocks representing different characteristics like nadir-only, oblique and high-resolution imagery. Second, during these investigations we demonstrate the benefit of integrating depth priors from tie-point measures, which are provided during presupposed Bundle Block Adjustment. Our work is based on the state-of-the-art framework VolSDF, which models 3D scenes by signed distance functions (SDFs), since this is more applicable for surface reconstruction compared to the standard volumetric representation in vanilla NeRFs. For evaluation, the NeRF-based reconstructions are compared to results of a publicly available benchmark dataset for airborne images.

Real-world PET PSMA imaging patterns in a Chicago safety-net hospital.

e17009 Background: Prostate-specific membrane antigen positron emission tomography (PSMA PET) imaging is an FDA-approved imaging modality with increased sensitivity for prostate cancer (PCa) detection. It thus can more accurately stage patients to determine optimal initial treatment. However, patients with indications for these scans can still experience significant delays in obtaining them, mainly due to insurance denials. Such delays often disproportionately affect those with healthcare disparities, representing a substantial population at University of Illinois Chicago (UIC), a safety-net hospital. We sought to identify patterns, barriers, & areas for improvement in obtaining this guideline-directed imaging modality. Methods: PCa patients with a PSMA PET scan ordered between January 2021 and June 2023 were included for analysis. Key medical and demographic data such as age, race/ethnicity, and insurance status were obtained. We also determined the time between ordering and obtaining the PSMA PET, and whether the scan led to classifying the patient’s disease as clinically localized, regional, or metastatic. We also determined whether subsequent treatment was curative or palliative intent. The dates and types of other imaging modalities, if obtained, were also recorded. Results: We identified 76 patients with PSMA PET scans ordered during the study timeframe. The mean age was 68.6 years, with a standard deviation of 7.3 years. 60.0% identified as Black or African American, 24.0% as White, 4.0% as Asian, and 1.3% as American Indian or Alaska Native. 40.8% had Illinois Medicare as their primary insurance, 27.6% had Medicaid, and 31.6% had another medical insurance. Most individuals (77.6%) had a delay in obtaining their PSMA PET scan, with a median time to scan of 23.0 days (range 6-136). All insurance types, including Illinois Medicare, Medicaid, and private insurance, had delays (74.2%, 81.0% and 79.2% of all PSMA PETs, respectively). Many delayed individuals also received conventional imaging (46.0%) within 3 months of the PSMA PET scan order as part of their workup, with 40.0% of PSMA PETs revealing new findings compared to the other scans. Ultimately, most of the imaged patients (54.0%) were determined to have regional or metastatic PCa. Delays in obtaining and receiving the results of scans affected individuals who underwent both curative (40.8%) as well as palliative intent (46.1%) treatment. Conclusions: Between January 2021 and June 2023, PCa patients had a median delay of approximately three weeks in obtaining a PSMA PET scan that was ordered urgently for the determination of their PCa treatment. Such delays can significantly impact health outcomes and often result in redundant conventional imaging. This retrospective study represents the first step in a quality improvement project to reduce delays to PSMA PETs and enable the development of a timely treatment plan to improve patient outcomes in our diverse patient population.

Improved weighted nuclear norm with total variation for removing multiplicative noise

This paper introduces an improved weighted nuclear norm with a total variation model tailored for removing multiplicative noise. The model incorporates a weight matrix to regularize the residual matrix, effectively leveraging image redundancy to differentiate various statistical properties of the noise. Since there is no guarantee of a unique solution, the model is reformulated as a linear equality constraint problem and decomposed into two subproblems. These are addressed by using the alternating direction method of multipliers and the split Bregman method, respectively. In addition, each alternative update step has a closed-form and convergent solution. After obtaining the denoised image in the log-domain, the recovered image is given by using the exponential function and bias correction. Experimental evaluations demonstrate the efficacy of our algorithms in enhancing image restoration quality.

Multisystem factors contributing to redundant intracranial vascular imaging in the ED.

To evaluate the multisystem factors contributing to redundant neurovascular orders in the ED. This was an IRB-approved, retrospective study, performed at a single institution examining a 5-year history of redundant CTA/MRA head and neck (HN) exams performed in the ED for patients with no documented clinical change in mental status/neurological exam necessitating additional imaging. Factors contributing to redundant ordering including provider experience, synchronous order placement, and radiologist recommendations were examined. Additionally, the impact of duplicative imaging in terms of medical cost and ED length of stay was evaluated. 250 patients met inclusion criteria with both CTA/MRA of the HN performed during a single ED encounter (total 500 exams). 190 (76%) redundant exams were not recommended by a radiologist and contributed to an added ED length of stay of 3.6h on average. Provider experience was not a significant contributing factor. 60 (24%) of redundant exams were recommended by a radiologist and were most frequently CTAs needed to clarify an area of artifact/high-grade stenosis/occlusion on a primary MRA exam. Evaluation of contributing factors to redundant CTA/MRA HN exams ordering has highlighted multiple associated factors including provider experience, recommendations by radiologists for clarification of MRA findings, as well as systems processes related to synchronous CTA/MRA order placement.

Reversible data hiding using morphology based pixel classification

Pixel value ordering (PVO) prediction has become a research hotspot in reversible data hiding community and existing achievements have proved the necessity of predicting more pixels in a block. The increase in predicted pixels is beneficial to fully exploiting the redundancy of the cover image, but a single reference pixel may lead to decreased prediction quality. Given this situation, this paper proposes to develop PVO in correlated pixels construction and introduce morphology as an important theoretical tool. Several largest pixels in a block are first selected as candidate reference pixels which are to be activated in descending order. Except for the last one, each active reference pixel is used to predict all pixels to generate a correlation matrix, on which the dilation operation is carried out to pick out connected pixels that are likely to be strongly correlated with the reference pixel. Whether to activate the next reference pixel depends on the presence of disconnected pixels. Once the last reference pixel is activated, all remaining pixels will be taken as connected pixels. After the first round of embedding ends, several smallest pixels are similarly selected as candidate reference pixels to realize the second round of embedding. Experimental results demonstrate that satisfactory superiority in fidelity is achieved, e.g., the average PSNR for the Kodak image database is 63.85 dB with an embedding capacity of 10,000 bits.

Evaluation of Mosaic Image Quality and Analysis of Influencing Factors Based on UAVs

With the growing prominence of UAV-based low-altitude remote sensing in agriculture, the acquisition and processing of high-quality UAV remote sensing images is paramount. The purpose of this study is to investigate the impact of various parameter settings on image quality and optimize these parameters for UAV operations to enhance efficiency and image quality. The study examined the effects of three parameter settings (exposure time, flight altitudes and forward overlap (OF)) on image quality and assessed images obtained under various conditions using signal-to-noise ratio (SNR) and BRISQUE algorithms. The results indicate that the setting of exposure time during UAV image acquisition directly affects image quality, with shorter exposure times resulting in lower SNR. The optimal exposure times for the RGB and MS cameras have been determined as 0.8 ms to 1.1 ms and 4 ms to 16 ms, respectively. Additionally, the best image quality is observed at flight altitudes between 15 and 35 m. The setting of UAV OF complements exposure time and flight altitude; to ensure the completeness of image acquisition, it is suggested that the flight OF is set to approximately 75% at a flight altitude of 25 m. Finally, the proposed image redundancy removal method has been demonstrated as a feasible approach for reducing image mosaicking time (by 84%) and enhancing the quality of stitched images (by 14%). This research has the potential to reduce flight costs, improve image quality, and significantly enhance agricultural production efficiency.

A Study for Design of Lightweight Dense Connection Network on Hyperspectral Image Classification

The characteristics of hyperspectral remote sensing images such as inconspicuous feature representativeness, single feature level, and complex information content, can lead to unstable classification results. We propose a lightweight dense network model that injects channel attention in the form of dense connections between network layers (DSE-DN) for the classification of hyperspectral images. In the DSE-DN network, principal component analysis (PCA) is applied to reduce redundancy in the hyperspectral images. Subsequently, a densely connected network is constructed, incorporating channel attention mechanisms through dense connections to enhance the analysis of spectral image features. Finally, the processed hyperspectral images are classified using a fully interconnected layer. We assess two classical hyperspectral datasets and construct 2DCNN, 3DCNN, ResNet, and the network that injects channel attention layer by layer to compare with DSE-DN. The experimental results indicate the utility of the DSE-DN network in hyperspectral image classification and its superiority over other networks.

Scaling and Masking: A New Paradigm of Data Sampling for Image and Video Quality Assessment

Quality assessment of images and videos emphasizes both local details and global semantics, whereas general data sampling methods (e.g., resizing, cropping or grid-based fragment) fail to catch them simultaneously. To address the deficiency, current approaches have to adopt multi-branch models and take as input the multi-resolution data, which burdens the model complexity. In this work, instead of stacking up models, a more elegant data sampling method (named as SAMA, scaling and masking) is explored, which compacts both the local and global content in a regular input size. The basic idea is to scale the data into a pyramid first, and reduce the pyramid into a regular data dimension with a masking strategy. Benefiting from the spatial and temporal redundancy in images and videos, the processed data maintains the multi-scale characteristics with a regular input size, thus can be processed by a single-branch model. We verify the sampling method in image and video quality assessment. Experiments show that our sampling method can improve the performance of current single-branch models significantly, and achieves competitive performance to the multi-branch models without extra model complexity. The source code will be available at https://github.com/Sissuire/SAMA.

In situ edge extraction enabled by reconfigurable van der Waals infrared photodetectors

At present, the widely used artificial intelligence image perception technology is composed of discrete detection and processing components, which is mismatching the demand for increasingly sizeable redundant image information processing. The transmission of non-critical information between components limits the efficiency of image perception systems. Inspired by the human vision system, which can extract the key features and reduce the transmission burden at the low-level detection end, we propose a symmetric structured mixed-dimensional n/p/n [n-molybdenum disulfide (MoS2)/p-germanium (Ge)/n-MoS2] interdigital van der Waals heterojunction infrared photodetector that can be tuned by bias voltage in response polarity and magnitude. The MoS2/Ge/MoS2 infrared photodetector has a bias-symmetric optoelectronic response and covers the detection band from visible to short-wave infrared. Through the joint detection of a pair of MoS2/Ge/MoS2 infrared photodetectors, we demonstrate the image in situ edge extraction at the detection end, which provides key features for high-level processing. This work has discovered the potential of retina-inspired infrared photodetectors on a 2D/3D integration platform, providing distinct opportunities for a neuromorphic visual perception hardware.

Counter‐Propagating Evanescent Illumination Super‐Resolution Chip

Super‐resolution chip (SRC) made of fluorescent polymer film and polygon film waveguide can realize subdiffraction imaging. However, the propagation losses of evanescent waves impose a serious restriction on imaging performance. Meanwhile, the required redundant raw images hinder the imaging speed. Multiple‐azimuths evanescent illumination at the same time can efficiently increase the illumination intensity and uniformity, and reduce the number of required raw images. But, the experimental realization is impeded by the complex spatial frequency mixing problem. Herein, an SRC microscopy method with counter‐propagating evanescent illumination is demonstrated, which circumvents the influence of complex spatial frequency mixing, and efficiently enhances the reconstructed results. Meanwhile, the proposed method reduces the number of required raw images by half and saves the image acquisition time, which benefits the imaging speed enhancement of the SRC microscopy system and promotes its future practical application.

Non-probability sampling network based on anomaly pedestrian trajectory discrimination for pedestrian trajectory prediction

Pedestrian trajectory prediction in first-person view is an important support for achieving fully automated driving in cities. However, existing pedestrian trajectory prediction methods still have significant shortcomings in terms of pedestrian trajectory diversity, dynamic scene constraints, and dependence on long-term trajectory prediction. We proposes a non-probability sampling network based on pedestrian trajectory anomaly recognition (ADsampler) to predict multiple possible future pedestrian trajectories. First, by incorporating pose and optical flow information, ADsampler models the multi-dimensional motion characteristics of pedestrians based on observed trajectory information and discriminates trajectory states. The sampling range in the Gaussian latent space is determined based on the recognition results. Next, velocity and yaw information of the car are introduced to model the car's motion state. A subtraction fusion network is employed to remove redundant image feature constraints in highly dynamic scenes. Finally, ADsampler utilizes a novel trajectory decoding network that combines the position encoding capability of GRU with the long-term dependency capturing ability of Transformer to decode and predict the fused features. we evaluate our model on crowded videos in the public datasets JAAD, PIE, ETH and UCY. Experiments demonstrate that the proposed method outperforms state-of-the-art approaches in prediction accuracy.

Reliable Breast Cancer Diagnosis with Deep Learning: DCGAN-Driven Mammogram Synthesis and Validity Assessment

Breast cancer imaging is paramount to quickly detecting and accurately evaluating the disease. The scarcity of annotated mammogram data presents a significant obstacle when building deep learning models that can produce reliable outcomes. This paper proposes a novel approach that utilizes deep convolutional generative adversarial networks (DCGANs) to effectively tackle the issue of limited data availability. The main goal is to produce synthetic mammograms that accurately reproduce the intrinsic patterns observed in real data, enhancing the current dataset. The proposed synthesis method is supported by thorough experimentation, demonstrating its ability to reproduce diverse viewpoints of the breast accurately. A mean similarity assessment with a standard deviation was performed to evaluate the credibility of the synthesized images and establish the clinical significance of the data obtained. A thorough evaluation of the uniformity within each class was conducted, and any deviations from each class’s mean values were measured. Including outlier removal using a specified threshold is a crucial process element. This procedure improves the accuracy level of each image cluster and strengthens the synthetic dataset’s general dependability. The visualization of the class clustering results highlights the alignment between the produced images and the inherent distribution of the data. After removing outliers, distinct and consistent clusters of homogeneous data points were observed. The proposed similarity assessment demonstrates noteworthy effectiveness, eliminating redundant and dissimilar images from all classes. Specifically, there are 505 instances in the normal class, 495 instances in the benign class, and 490 instances in the malignant class out of 600 synthetic mammograms for each class. To check the further validity of the proposed model, human experts visually inspected and validated synthetic images. This highlights the effectiveness of our methodology in identifying substantial outliers.

Fast Multi-View 3D reconstruction of seedlings based on automatic viewpoint planning

Three-dimensional models of plants provide valuable phenotypic information. Phenotypic measurements of plant structures are essential for monitoring plant growth and understanding plant responses to environmental changes. Existing 3D reconstruction techniques have achieved accurate reconstruction models of some plants such as corn and soybeans. However, several drawbacks exist in current plant 3D reconstruction systems, including high cost, fixed capture perspectives and complex operation. To address these issues and considering the influence of camera capture angles on model reconstruction accuracy, we investigated a viewpoint planning reconstruction method. We designed a plant seedling reconstruction system that utilizes a consumer-grade L515 LiDAR sensor and a precision turntable. We establish a viewpoint rule based on the camera imaging model of the three-dimensional spatial structure of leaves, constructing minimum constraints on the leaf normal vector and the camera optical axis. This enables to determine the optimal capture viewpoint for each leaf to obtain the maximum leaf information. The angle β between the leaf normal vector and the camera optical axis serves as the basis for the turntable rotation, systematically planning the rotation of the turntable to enable the camera to capture the maximum useful leaf information. Unlike static camera-based turntable reconstruction systems that capture images at fixed angle intervals, we plan the rotation of the turntable based on the acquired information. This allows us to obtain more effective point cloud information of plant seedlings from the same or even fewer images, reducing the collection of redundant images and reconstructing more accurate plant seedling 3D models. Additionally, we measured commonly used leaf traits in plant phenotypic studies, such as leaf length, leaf width, and leaf area. The leaf area measured based on the reconstructed seedling model exhibited high accuracy (R2 > 0.99). The results of this study demonstrate that the consumer-grade L515 LiDAR sensor combined with the proposed viewpoint planning method can effectively reconstruct accurate seedling 3D models and measure phenotypic information. Due to the absence of error accumulation caused by adjacent view registration, this approach offers advantages such as shorter reconstruction time, higher efficiency and increased accuracy compared to some multi-view point cloud registration and reconstruction methods. Therefore, this system can be applied to three-dimensional reconstruction and phenotype analysis of plant seedlings due to its high efficiency and accuracy.