Can 3D point cloud data improve automated body condition score prediction in dairy cattle?

• URL: http://arxiv.org/abs/2601.22522v2
• Date: Tue, 03 Feb 2026 04:16:16 GMT
• Title: Can 3D point cloud data improve automated body condition score prediction in dairy cattle?
• Abstract summary: Body condition score (BCS) is a widely used indicator of body energy status and is closely associated with metabolic status, reproductive performance, and health in dairy cattle.<n>Computer vision approaches have been applied to BCS prediction, with depth images widely used because they capture geometric information independent of coat color and texture.<n>Three-dimensional point cloud data have attracted increasing interest due to their ability to represent richer geometric characteristics of animal morphology.
• Score: 5.1089385151950655
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Body condition score (BCS) is a widely used indicator of body energy status and is closely associated with metabolic status, reproductive performance, and health in dairy cattle; however, conventional visual scoring is subjective and labor-intensive. Computer vision approaches have been applied to BCS prediction, with depth images widely used because they capture geometric information independent of coat color and texture. More recently, three-dimensional point cloud data have attracted increasing interest due to their ability to represent richer geometric characteristics of animal morphology, but direct head-to-head comparisons with depth image-based approaches remain limited. In this study, we compared top-view depth image and point cloud data for BCS prediction under four settings: 1) unsegmented raw data, 2) segmented full-body data, 3) segmented hindquarter data, and 4) handcrafted feature data. Prediction models were evaluated using data from 1,020 dairy cows collected on a commercial farm, with cow-level cross-validation to prevent data leakage. Depth image-based models consistently achieved higher accuracy than point cloud-based models when unsegmented raw data and segmented full-body data were used, whereas comparable performance was observed when segmented hindquarter data were used. Both depth image and point cloud approaches showed reduced accuracy when handcrafted feature data were employed compared with the other settings. Overall, point cloud-based predictions were more sensitive to noise and model architecture than depth image-based predictions. Taken together, these results indicate that three-dimensional point clouds do not provide a consistent advantage over depth images for BCS prediction in dairy cattle under the evaluated conditions.

Related papers

• Evaluating transfer learning strategies for improving dairy cattle body weight prediction in small farms using depth-image and point-cloud data [2.9716917288738283]
  The aim of this study was to evaluate whether transfer learning enhances body weight prediction on a small farm with limited data.<n>Four deep learning models were evaluated: ConvNeXt and MobileViT for depth images, PointNet and DGCNN for point clouds.<n>Results indicate that pretrained representations generalize well across farms with differing imaging conditions and dairy cattle populations.
  arXiv  Detail & Related papers  (2026-01-03T02:41:54Z)
• PointVoxelFormer -- Reviving point cloud networks for 3D medical imaging [0.0]
  Point clouds are a very efficient way to represent volumetric data in medical imaging.<n>Despite their benefits, point clouds are still underexplored in medical imaging compared to volumetric 3D CNNs and vision transformers.<n>This work presents a hybrid approach that combines point-wise operations with intermediate differentiableisation and dense localised CNNs.
  arXiv  Detail & Related papers  (2024-12-23T08:43:39Z)
• Universal Bovine Identification via Depth Data and Deep Metric Learning [1.6605913858547239]
  This paper proposes and evaluates, for the first time, a depth-only deep learning system for accurately identifying individual cattle. An increase in herd size skews the cow-to-human ratio at the farm and makes the manual monitoring of individuals more challenging. Underpinned by our previous work, this paper introduces a deep-metric learning method for cattle identification using depth data from an off-the-shelf 3D camera.
  arXiv  Detail & Related papers  (2024-03-29T22:03:53Z)
• Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
  We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task. Our approach involves making initial predictions of 2D semantic masks using different large vision models. To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
  arXiv  Detail & Related papers  (2023-11-03T15:41:15Z)
• Intrinsic Image Decomposition Using Point Cloud Representation [13.771632868567277]
  We introduce Point Intrinsic Net (PoInt-Net), which leverages 3D point cloud data to concurrently estimate albedo and shading maps. PoInt-Net is efficient, achieving consistent performance across point clouds of any size with training only required on small-scale point clouds.
  arXiv  Detail & Related papers  (2023-07-20T14:51:28Z)
• OOD-CV-v2: An extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images [59.51657161097337]
  OOD-CV-v2 is a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions. In addition to this novel dataset, we contribute extensive experiments using popular baseline methods.
  arXiv  Detail & Related papers  (2023-04-17T20:39:25Z)
• Point Cloud Registration for LiDAR and Photogrammetric Data: a Critical Synthesis and Performance Analysis on Classic and Deep Learning Algorithms [7.874736360019618]
  We provide a comprehensive review of the state-of-the-art (SOTA) point cloud registration methods. We analyze and evaluate these methods using a diverse set of point cloud data from indoor to satellite sources. More than ten methods, including classic hand-crafted, deep-learning-based feature correspondence, and robust C2C methods were tested.
  arXiv  Detail & Related papers  (2023-02-14T16:52:26Z)
• 3D Point Cloud Pre-training with Knowledge Distillation from 2D Images [128.40422211090078]
  We propose a knowledge distillation method for 3D point cloud pre-trained models to acquire knowledge directly from the 2D representation learning model. Specifically, we introduce a cross-attention mechanism to extract concept features from 3D point cloud and compare them with the semantic information from 2D images. In this scheme, the point cloud pre-trained models learn directly from rich information contained in 2D teacher models.
  arXiv  Detail & Related papers  (2022-12-17T23:21:04Z)
• SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds [52.624157840253204]
  We introduce SensatUrban, an urban-scale UAV photogrammetry point cloud dataset consisting of nearly three billion points collected from three UK cities, covering 7.6 km2. Each point in the dataset has been labelled with fine-grained semantic annotations, resulting in a dataset that is three times the size of the previous existing largest photogrammetric point cloud dataset.
  arXiv  Detail & Related papers  (2022-01-12T14:48:11Z)
• Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation [74.76155168705975]
  Deep Bingham Networks (DBN) can handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. DBN extends the state of the art direct pose regression networks by (i) a multi-hypotheses prediction head which can yield different distribution modes. We propose new training strategies so as to avoid mode or posterior collapse during training and to improve numerical stability.
  arXiv  Detail & Related papers  (2020-12-20T19:20:26Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.