Estimating the Diameter at Breast Height of Trees in a Forest from RGB
General Robotics, Automation, Sensing and Perception (GRASP) Laboratory, University of Pennsylvania（通用机器人、自动化、传感与感知实验室，宾夕法尼亚大学）；Department of Forest Resources and Environmental Conservation, Virginia Tech（森林资源与环境保护系，弗吉尼亚理工学院）
Siming He, Zachary Osman, Fernando Cladera, Dexter Ong, Nitant Rai, Patrick Corey Green, Vijay Kumar, Pratik Chaudhari
Forest inventories rely on accurate measurements of the diameter at breast height (DBH) for ecological monitoring, resource management, and carbon accounting. While LiDAR-based techniques can achieve centimeter-level precision, they are cost-prohibitive and operationally complex. We present a low-cost alternative that only needs a consumer-grade 360 video camera. Our semi-automated pipeline comprises of (i) a dense point cloud reconstruction using Structure from Motion (SfM) photogrammetry software called Agisoft Metashape, (ii) semantic trunk segmentation by projecting Grounded Segment Anything (SAM) masks onto the 3D cloud, and (iii) a robust RANSAC-based technique to estimate cross section shape and DBH. We introduce an interactive visualization tool for inspecting segmented trees and their estimated DBH. On 61 acquisitions of 43 trees under a variety of conditions, our method attains median absolute relative errors of 5-9% with respect to "ground-truth" manual measurements. This is only 2-4% higher than LiDAR-based estimates, while employing a single 360 camera that costs orders of magnitude less, requires minimal setup, and is widely available.
University College London（伦敦大学学院）；Royal Free & University College Medical School（皇家自由与大学学院医学院）；Royal Free London Foundation Trust（皇家自由伦敦基金会信托）
Breast cancer is the most prevalent cancer type in women, and while its survival rate is generally high the aesthetic outcome is an increasingly important factor when evaluating different treatment alternatives. 3D scanning and reconstruction techniques offer a flexible tool for building detailed and accurate 3D breast models that can be used both pre-operatively for surgical planning and post-operatively for aesthetic evaluation. This paper aims at comparing the accuracy of low-cost 3D scanning technologies with the significantly more expensive state-of-the-art 3D commercial scanners in the c…
University of Bologna（博洛尼亚大学）；University of Oxford（牛津大学）；FiveAI Ltd（FiveAI有限公司）；Google Inc.（谷歌公司）
Camera pose estimation is an important problem in computer vision. Common techniques either match the current image against keyframes with known poses, directly regress the pose, or establish correspondences between keypoints in the image and points in the scene to estimate the pose. In recent years, regression forests have become a popular alternative to establish such correspondences. They achieve accurate results, but have traditionally needed to be trained offline on the target scene, preventing relocalisation in new environments. Recently, we showed how to circumvent this limitation by ad…
Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation
Beijing Normal University（北京师范大学）；CSIRO Environment（英联邦科学与工业研究组织环境部）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Wuhan University（武汉大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Yet, existing 3D torso scanners are expensive and either infrastructure-heavy or subject to motion artefacts. In this pap…
Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction From Monocular RGB Videos
Regensburg Medical Image Computing (ReMIC), OTH Regensburg（雷根斯堡应用技术大学雷根斯堡医学图像计算中心）；Department for Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg（雷根斯堡大学医院整形、手与重建外科）
Comparative Analysis of Novel View Synthesis and Photogrammetry for 3D Forest Stand Reconstruction and extraction of individual tree parameters
Fuzhou University（福州大学）；National Engineering Research Center of Geospatial Information Technology（国家地理空间信息技术工程研究中心）；Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education（教育部空间数据挖掘与信息共享重点实验室）；The Academy of Digital China (Fujian)（数字中国研究院（福建））
Mapping Semantic Segmentation to Point Clouds Using Structure from Motion for Forest Analysis
Universidad de Buenos Aires（布宜诺斯艾利斯大学）；Facultad de Ciencias Exactas y Naturales（精确科学与自然学院）；CONICET-Universidad de Buenos Aires（CONICET-布宜诺斯艾利斯大学）；Instituto de Ciencias de la Computación (ICC)（计算科学研究所）
Institute of Science, Beijing Forestry University（北京林业大学理学院）
Terrestrial laser scanner is a kind of fast, high-precision data acquisition device, which had been more and more applied to the research areas of forest inventory. In this study, a kind of automated low-cost terrestrial laser scanner was designed and implemented based on a two-dimensional laser radar sensor SICK LMS-511 and a stepper motor. The new scanner was named as BEE, which can scan the forest trees in three dimension. The BEE scanner and its supporting software are specifically designed for forest inventory. The experiments have been performed by using the BEE scanner in an artificial …
A large number of studies analyse object detection and pose estimation at visual level in 2D, discussing the effects of challenges such as occlusion, clutter, texture, etc., on the performances of the methods, which work in the context of RGB modality. Interpreting the depth data, the study in this paper presents thorough multi-modal analyses. It discusses the above-mentioned challenges for full 6D object pose estimation in RGB-D images comparing the performances of several 6D detectors in order to answer the following questions: What is the current position of the computer vision community fo…
A Lightweight Fiducial-Based Pipeline for 3D Hyperspectral Mapping of ex-vivo Lumpectomy Specimens
Politecnico di Milano（米兰理工大学）；Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano（米兰理工大学电子、信息与生物工程系）；Department of Physics, Politecnico di Milano（米兰理工大学物理系）
Automated driving technology has gained a lot of momentum in the last few years. For the exploration field, navigation is the important key for autonomous operation. In difficult scenarios such as snowy environment, the road is covered with snow and road detection is impossible in this situation using only basic techniques. This paper introduces detection of snowy road in forest environment using RGB camera. The method combines noise filtering technique with morphological operation to classify the image component. By using the assumption that all road is covered by snow and the snow part is de…
We address the problem of estimating the pose of humans using RGB image input. More specifically, we are using a random forest classifier to classify pixels into joint-based body part categories, much similar to the famous Kinect pose estimator [11], [12]. However, we are using pure RGB input, i.e. no depth. Since the random forest requires a large number of training examples, we are using computer graphics generated, synthetic training data. In addition, we assume that we have access to a large number of real images with bounding box labels, extracted for example by a pedestrian detector or a…
Event Spectroscopy: Event-based Multispectral and Depth Sensing using Structured Light
Environmental Robotics Laboratory, Dep. of Environmental Systems Science, ETH Zurich（环境机器人实验室，环境系统科学系，苏黎世联邦理工学院）；Swiss Federal Institute for Forest, Snow and Landscape Research (WSL)（瑞士森林、雪和景观研究联邦研究所）；Robotics and Perception Group, University of Zurich（机器人与感知小组，苏黎世大学）
A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method
Evaluating the point cloud of individual trees generated from images based on Neural Radiance fields (NeRF) method
Fuzhou University（福州大学）；National Engineering Research Center of Geospatial Information Technology（国家地理空间信息技术工程研究中心）；Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education（空间数据挖掘与信息共享教育部重点实验室）；The Academy of Digital China (Fujian)（数字中国研究院（福建））
Institute for Computing, Information and Cognitive Systems (ICICS), The University of British Columbia（不列颠哥伦比亚大学计算、信息与认知系统研究所）；Perceptive Inc（Perceptive公司）
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection. Recent random forests based methods exploit randomly sampled pixel comparison features to predict 3D world locations for 2D image locations to guide the camera pose optimization. However, these image features are only sampled randomly in the images, without considering the spatial structures or geometric information, leading to large errors or failure cases with the existence of poorly textured areas or in motion blur. Line…
Manual Labelling Artificially Inflates Deep Learning-Based Segmentation Performance on RGB Images of Closed Canopy: Validation Using TLS
University of Cambridge（剑桥大学）；Queen Mary University of London（伦敦玛丽女王大学）；Digital Environment Research Institute, Queen Mary University of London（伦敦玛丽女王大学数字环境研究所）
Breast cancer is a disease that threatens many women's life, thus, early and accurate detection plays a key role in reducing the mortality rate. Mammography stands as the reference technique for breast cancer screening; nevertheless, many countries still lack access to mammograms due to economic, social, and cultural issues. Last advances in computational tools, infrared cameras, and devices for bio-impedance quantification allowed the development of parallel techniques like thermography, infrared imaging, and electrical impedance tomography, these being faster, reliable and cheaper. In the la…
Fudan University（复旦大学）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）
The performance of medical image classification has been enhanced by deep convolutional neural networks (CNNs), which are typically trained with cross-entropy (CE) loss. However, when the label presents an intrinsic ordinal property in nature, e.g., the development from benign to malignant tumor, CE loss cannot take into account such ordinal information to allow for better generalization. To improve model generalization with ordinal information, we propose a novel meta ordinal regression forest (MORF) method for medical image classification with ordinal labels, which learns the ordinal relatio…
Smartphone-based Circular Plot Sampling for Forest Inventory
School of Applied and Creative Computing, Purdue University（应用与创意计算学院，普渡大学）；Department of Forestry and Natural Resources, Purdue University（林业与自然资源学院，普渡大学）
The University of British Columbia（不列颠哥伦比亚大学）；Perceptive Inc.（Perceptive 公司）
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection. Recent random forests based methods directly predict 3D world locations for 2D image locations to guide the camera pose optimization. During training, each tree greedily splits the samples to minimize the spatial variance. However, these greedy splits often produce uneven sub-trees in training or incorrect 2D-3D correspondences in testing. To address these problems, we propose a sample-balanced objective to encourage equal…
FiveAI Ltd.（FiveAI有限公司）；University of Oxford（牛津大学）
Many applications require a camera to be relocalised online, without expensive offline training on the target scene. Whilst both keyframe and sparse keypoint matching methods can be used online, the former often fail away from the training trajectory, and the latter can struggle in textureless regions. By contrast, scene coordinate regression (SCoRe) methods generalise to novel poses and can leverage dense correspondences to improve robustness, and recent work has shown how to adapt SCoRe forests between scenes, allowing their state-of-the-art performance to be leveraged online. However, becau…
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；University of Warwick（华威大学）；New York University（纽约大学）；WWF Switzerland（世界自然基金会瑞士分会）
TreeLoc: 6-DoF LiDAR Global Localization in Forests via Inter-Tree Geometric Matching
Dept. of Mechanical Engineering, SNU（机械工程系，首尔国立大学）；Oxford Robotics Institute, University of Oxford（牛津大学机器人研究所）；CSIRO Robotics, DATA61, CSIRO and the School of Electrical Engineering and Computer Science, The University of Queensland (UQ)（CSIRO机器人、DATA61、CSIRO及昆士兰大学电气工程与计算机科学学院）
Vision-based segmentation in forested environments is a key functionality for autonomous forestry operations such as tree felling and forwarding. Deep learning algorithms demonstrate promising results to perform visual tasks such as object detection. However, the supervised learning process of these algorithms requires annotations from a large diversity of images. In this work, we propose to use simulated forest environments to automatically generate 43 k realistic synthetic images with pixel-level annotations, and use it to train deep learning algorithms for tree detection. This allows us to …
University of Oxford（牛津大学）；Technische Universität Dresden（德累斯顿工业大学）
This work addresses the task of camera localization in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step, Random Forests (RFs) are typically used. On the other hand, Neural Networks (NNs) reign in many dense regression tasks, but are not test-time efficient. We ask the question: which of the two is best for camera localization? To address this, we make …
Task Planning Support for Arborists and Foresters: Comparing Deep Learning Approaches for Tree Inventory and Tree Vitality Assessment Based on UAV-Data
Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery
DeepForest Technologies Co., Ltd.（深森林技术有限公司）；YM Lab.（YM实验室）；Graduate School of Agriculture, Kyoto University（京都大学农业研究院）；Graduate School of Science, Osaka Metropolitan University（大阪 metropolitan 大学理学研究院）；Faculty of Tropical Forestry, Universiti Malaysia Sabah（马来西亚沙巴大学热带林业学院）；Forest Research Centre, Sabah Forestry Department（沙巴林业部门森林研究中心）
Kyoto University（京都大学）；Japan Science and Technology Agency（日本科学技术振兴机构）
Automatic classification of trees using remotely sensed data has been a dream of many scientists and land use managers. Recently, Unmanned aerial vehicles (UAV) has been expected to be an easy-to-use, cost-effective tool for remote sensing of forests, and deep learning has attracted attention for its ability concerning machine vision. In this study, using a commercially available UAV and a publicly available package for deep learning, we constructed a machine vision system for the automatic classification of trees. In our method, we segmented a UAV photography image of forest into individual t…
Low-Cost Tree Crown Dieback Estimation Using Deep Learning-Based Segmentation
University of Cambridge（剑桥大学）；Universidad de Alcalá（阿尔卡拉大学）；Institute of Forest Sciences (INIA-CSIC)（森林科学研究所（INIA-CSIC））；Queen Mary University of London（伦敦玛丽女王大学）；Digital Environment Research Institute（数字环境研究所）
Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute（遥感与摄影测量系，芬兰地理研究机构）；Department of Computer Science, Aalto University（计算机科学系，阿alto大学）
Automated HER2 scoring with uncertainty quantification using lensfree holography and deep learning
Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校电气与计算机工程系）；Bioengineering Department, University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校生物工程系）；California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校加州纳米系统研究所）；Department of Computer Science, University of California, Los Angeles, CA, 90095, USA（加州大学洛杉矶分校计算机科学系）
