labelCloud: A Lightweight Labeling Tool for Domain-Agnostic 3D Object Detection in Point Clouds

Sager, Christoph; Zschech, Patrick; Kuehl, Niklas

doi:10.14733/cadaps.2022.1191-1206

Cited by 10 publications

(3 citation statements)

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“…By employing a point cloud labelling tool [23], the manual extraction of the rump part from the point cloud data of dairy cows is undertaken with precision and purpose. This meticulous process involves human intervention to isolate and delineate the specific anatomical region corresponding to the rump in each scanned cow.…”

Section: Rump Part Extractionmentioning

confidence: 99%

Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging

Lee,

Alam

et al. 2024

Preprint

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This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.

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Section: Rump Part Extractionmentioning

confidence: 99%

Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging

Lee,

Alam

et al. 2024

Preprint

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“…For each object, four different viewpoints were collected for each of the four articulation states for the object. We measure the real joint state and annotate the data using [30] with orientated 3D bound-ing boxes. In total, we collected 263 images.…”

Section: Datasetsmentioning

confidence: 99%

CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects

Nick¹,

Irshad²,

Захаров³

et al. 2023

Preprint

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“…The solution used here was to slice and crop data to more processable pieces. The sliced point cloud data set was annotated with the point cloud capable labelling tool, labelCloud [23] as visible in Fig. 4 with a sample point cloud crop to be added to training dataset.…”

Section: B Point Cloud Training Datamentioning

confidence: 99%

Deep Learning Machine Vision Solutions for Monitoring Safety Structures to Supplement Building Information Models

Aihkisalo,

Purhonen,

Niskanen

2023

2023 IEEE International Conference on Software Services Engineering (SSE)

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Building Information Models (BIM), provide a static view on building structures on design and construction time. During construction time, the safety structures and their positioning among conditions are vital for the safety of the employees on site. The models usually lack the temporospatial information regarding non-static safety structures as maintaining of such information manually is laborious. Thus, automated and continuous safety structure monitoring is costefficient and vital keeping the model up-to-date while improving construction site safety and risk information sharing among the construction related personnel during the project. This paper researches of providing up-to-date and supplementary safety structure information to building models by means of various deep learning-based machine vision solutions. The machine vision tasks consist of deep learning safety related object detection in images, point-clouds, and further instance segmentation to enable safety structure fitness determination by more traditional machine vision means

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labelCloud: A Lightweight Labeling Tool for Domain-Agnostic 3D Object Detection in Point Clouds

Cited by 10 publications

References 0 publications

Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging

Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging

CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects

Deep Learning Machine Vision Solutions for Monitoring Safety Structures to Supplement Building Information Models

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