Cover crops are becoming an increasingly important tool for weed suppression. Biomass production in cover crops is one of the most important predictors of weed suppressive ability. A significant challenge for growers is that cover crop growth can be patchy within fields, making biomass estimation difficult. This study tested ground-based structure-from-motion (SfM) for estimating and mapping cereal rye (Secale cereale L.) biomass. SfM generated 3D point clouds from red, green, and blue (RGB) videos collected by a handheld GoPro camera over five fields in North Carolina during the 2022 to 2023 winter season. A model for predicting biomass was generated by relating measured biomass at termination using a density–height index (DH) from point cloud pixel density multiplied by crop height. Overall biomass ranged from 320 to 9,200 kg ha−1, and crop height ranged from 10 to 120 cm. Measured biomass at termination was linearly related to DH (r2 = 0.813) through levels of 9,000 kg ha−1. Based on independent data validation, predicted biomass and measured biomass were linearly related (r2 = 0.713). In the field maps generated by kriging, measured biomass data were autocorrelated at a range of 5.4 to 42.2 m, and predicted biomass data were autocorrelated at a range of 3.4 to 12.0 m. However, the spatial arrangement of high- and low-performing areas was similar for predicted and measured biomass, particularly in fields with greatest patchiness and spatial correlation in biomass values. This study provides proof-of-concept that ground-based SfM can potentially be used to nondestructively estimate and map cover crop biomass production and identify low-performing areas at higher risk for weed pressure and escapes.

The research paper delves into the importance of point cloud data obtained from 3D scanning technology ensuring quality control in industrial settings. It presents a new heuristic approach that utilizes the wavelet algorithm and other techniques to detect and characterize induced forming defects accurately. The proposed approach offers more flexibility, ease of use, and better results based on descriptive and prescriptive analyses from DRM. The results demonstrate that the wavelet algorithm was successful in identifying and characterizing forming defects in point cloud data.

This study emphasizes the importance of automatic synthetic data generation in data-driven applications, especially in the development of a 3D computer vision system for engineering contexts such as brownfield factory projects, where no data is readily available. Key points: (1) A successful integration of a synthetic data generator with the S3DIS dataset, leading to a significant enhancement in object detection of previous classes and enabling recognition of new ones; (2) A proposal for a CAD-based configurator for efficient and customizable scene reconstruction from LiDAR scanner point clouds.

While finite element (FE) modeling is widely used for ultimate strength assessments of structural systems, incorporating complex distortions and imperfections into FE models remains a challenge. Conventional methods typically rely on assumptions about the periodicity of distortions through spectral or modal methods. However, these approaches are not viable under the many realistic scenarios where these assumptions are invalid. Research efforts have consistently demonstrated the ability of point cloud data, generated through laser scanning or photogrammetry-based methods, to accurately capture structural deformations at the millimeter scale. This enables the updating of numerical models to capture the exact structural configuration and initial imperfections without the need for unrealistic assumptions. This research article investigates the use of point cloud data for updating the initial distortions in a FE model of a stiffened ship deck panel, for the purposes of ultimate strength estimation. The presented approach has the additional benefit of being able to explicitly account for measurement uncertainty in the analysis. Calculations using the updated FE models are compared against ground truth test data as well as FE models updated using standard spectral methods. The results demonstrate strength estimation that is comparable to existing approaches, with the additional advantages of uncertainty quantification and applicability to a wider range of application scenarios.

Recent innovations in 3D imaging technology have created unprecedented potential for better understanding weed responses to management tactics. Although traditional 2D imaging methods for mapping weed populations can be limited in the field by factors such as shadows and tissue overlap, 3D imaging mitigates these challenges by using depth data to create accurate plant models. Three-dimensional imaging can be used to generate spatiotemporal maps of weed populations in the field and target weeds for site-specific weed management, including automated precision weed control. This technology will also help growers monitor cover crop performance for weed suppression and detect late-season weed escapes for timely control, thereby reducing seedbank persistence and slowing the evolution of herbicide resistance. In addition to its many applications in weed management, 3D imaging offers weed researchers new tools for understanding spatial and temporal heterogeneity in weed responses to integrated weed management tactics, including weed–crop competition and weed community dynamics. This technology will provide simple and low-cost tools for growers and researchers alike to better understand weed responses in diverse agronomic contexts, which will aid in reducing herbicide use, mitigating herbicide-resistance evolution, and improving environmental health.

We introduce the polygon cloud, a compressible representation of three-dimensional geometry (including attributes, such as color), intermediate between polygonal meshes and point clouds. Dynamic polygon clouds, like dynamic polygonal meshes and dynamic point clouds, can take advantage of temporal redundancy for compression. In this paper, we propose methods for compressing both static and dynamic polygon clouds, specifically triangle clouds. We compare triangle clouds to both triangle meshes and point clouds in terms of compression, for live captured dynamic colored geometry. We find that triangle clouds can be compressed nearly as well as triangle meshes, while being more robust to noise and other structures typically found in live captures, which violate the assumption of a smooth surface manifold, such as lines, points, and ragged boundaries. We also find that triangle clouds can be used to compress point clouds with significantly better performance than previously demonstrated point cloud compression methods. For intra-frame coding of geometry, our method improves upon octree-based intra-frame coding by a factor of 5–10 in bit rate. Inter-frame coding improves this by another factor of 2–5. Overall, our proposed method improves over the previous state-of-the-art in dynamic point cloud compression by 33% or more.

We investigate the estimation of the perimeter of a set by a graph cut of a random geometric graph. For Ω ⊆ D = (0, 1)d with d ≥ 2, we are given n random independent and identically distributed points on D whose membership in Ω is known. We consider the sample as a random geometric graph with connection distance ε > 0. We estimate the perimeter of Ω (relative to D) by the, appropriately rescaled, graph cut between the vertices in Ω and the vertices in D ∖ Ω. We obtain bias and variance estimates on the error, which are optimal in scaling with respect to n and ε. We consider two scaling regimes: the dense (when the average degree of the vertices goes to ∞) and the sparse one (when the degree goes to 0). In the dense regime, there is a crossover in the nature of the approximation at dimension d = 5: we show that in low dimensions d = 2, 3, 4 one can obtain confidence intervals for the approximation error, while in higher dimensions one can obtain only error estimates for testing the hypothesis that the perimeter is less than a given number.

Partial differential equations (PDE) on manifolds arise in many areas, including mathematics and many applied fields. Due to the complicated geometrical structure of the manifold, it is difficult to get efficient numerical method to solve PDE on manifold. In the paper, we propose a method called point integral method (PIM) to solve the Poisson-type equations from point clouds. Among different kinds of PDEs, the Poisson-type equations including the standard Poisson equation and the related eigenproblem of the Laplace-Beltrami operator are one of the most important. In PIM, the key idea is to derive the integral equations which approximates the Poisson-type equations and contains no derivatives but only the values of the unknown function. This feature makes the integral equation easy to be discretized from point cloud. In the paper, we explain the derivation of the integral equations, describe the point integral method and its implementation, and present the numerical experiments to demonstrate the convergence of PIM.

As various robots are anticipated to coexist with humans in the near future, safe manipulation in unknown, cluttered environments becomes an important issue. Manipulation in an unknown environment, however, has been proven to be NP-Hard and the risk of unexpected human--robot collision hampers the dawning of the era of human--robot coexistence. We propose a non-contact-based sensitive skin as a means to provide safe manipulation hardware and interleaving planning between the workspace and the configuration space as software to solve manipulation problems in unknown, crowded environments. Novelty of the paper resides in demonstration of real time and yet complete path planning in an uncertain and crowded environment. To that end, we introduce the framework of the sensor-based interleaving planner (SBIP) whereby search completeness and safe manipulation are both guaranteed in cluttered environments. We study an interleaving mechanism between sensation in a workspace and execution in the corresponding configuration space for real-time planning in uncertain environments, thus the name interleaving planner implies.

Applications of the proposed system include manipulators of a humanoid robot, surgical manipulators, and robotic manipulators working in hazardous and uncertain environments such as underwater, unexplored planets, and unstructured indoor spaces.