Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models
University of Southern California（南加州大学）；Institute for Creative Technologies（创意技术研究所）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；New York University（纽约大学）
Meida Chen, Andrew Feng, Kyle McCullough, Pratusha Bhuvana Prasad, Ryan McAlinden, Lucio Soibelman
发表信息：
Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2020
At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locations and ground materials (Chen et al., 2019). The ultimate goal is to create realistic virtual environments and provide the necessary information for simulation. We tested the generalizability of the previously proposed framework using a database created under the U.S. Army's One World Terrain (OWT) project with a variety of landscapes (i.e., various buildings styles, types of vegetation, and urban density) and different data qualities (i.e., flight altitudes and overlap between images). Although the database is considerably larger than existing databases, it remains unknown whether deep-learning algorithms have truly achieved their full potential in terms of accuracy, as sizable data sets for training and validation are currently lacking. Obtaining large annotated 3D point-cloud databases is time-consuming and labor-intensive, not only from a data annotation perspective in which the data must be manually labeled by well-trained personnel, but also from a raw data collection and processing perspective. Furthermore, it is generally difficult for segmentation models to differentiate objects, such as buildings and tree masses, and these types of scenarios do not always exist in the collected data set. Thus, the objective of this study is to investigate using synthetic photogrammetric data to substitute real-world data in training deep-learning algorithms. We have investigated methods for generating synthetic UAV-based photogrammetric data to provide a sufficiently sized database for training a deep-learning algorithm with the ability to enlarge the data size for scenarios in which deep-learning models have difficulties.
Recovering surface albedos from photogrammetric images for realistic rendering and synthetic environments can greatly facilitate its downstream applications in VR/AR/MR and digital twins. The textured 3D models from standard photogrammetric pipelines are suboptimal to these applications because these textures are directly derived from images, which intrinsically embedded the spatially and temporally variant environmental lighting information, such as the sun illumination, direction, causing different looks of the surface, making such models less realistic when used in 3D rendering under synthe…
Comparative evaluation of photogrammetric reconstruction methods and 3D Gaussian Splatting for road surface roughness analysis
School of International Education, Hubei University of Technology（湖北工业大学国际教育学院）；School of Computer Science, Hubei University of Technology（湖北工业大学计算机科学学院）
The Hong Kong Polytechnic University（香港理工大学）；Sensat Ltd.（Sensat有限公司）；Newcastle University（纽卡斯尔大学）；University of Oxford（牛津大学）
With the recent availability and affordability of commercial depth sensors and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets have been publicized to facilitate research in 3D computer vision. However, existing datasets either cover relatively small areas or have limited semantic annotations. Fine-grained understanding of urban-scale 3D scenes is still in its infancy. In this paper, 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 km^2. Each point in…
University of Southern California（南加州大学）；Institute for Creative Technologies（创意技术研究所）；US Army（美国陆军）
With state-of-the-art sensing and photogrammetric techniques, Microsoft Bing Maps team has created over 125 highly detailed 3D cities from 11 different countries that cover hundreds of thousands of square kilometer areas. The 3D city models were created using the photogrammetric technique with high-resolution images that were captured from aircraft-mounted cameras. Such a large 3D city database has caught the attention of the US Army for creating virtual simulation environments to support military operations. However, the 3D city models do not have semantic information such as buildings, veget…
Nomoko AG（Nomoko股份公司）；The Dalle Molle Institute for Artificial Intelligence (IDSIA)（达莱莫勒人工智能研究所（IDSIA））
We introduce a new outdoor urban 3D pointcloud dataset, covering a total area of 2.7 $km^2$, sampled from three Swiss cities with different characteristics. The dataset is manually annotated for semantic segmentation with per-point labels, and is built using photogrammetry from images acquired by multirotors equipped with high-resolution cameras. In contrast to datasets acquired with ground LiDAR sensors, the resulting point clouds are uniformly dense and complete, and are useful to disparate applications, including autonomous driving, gaming and smart city planning. As a benchmark, we report …
Corals are the primary habitat-building life-form on reefs that support a quarter of the species in the ocean. A coral reef ecosystem usually consists of reefs, each of which is like a tall building in any city. These reef-building corals secrete hard calcareous exoskeletons that give them structural rigidity, and are also a prerequisite for our accurate 3D modeling and semantic mapping using advanced photogrammetric computer vision and machine learning. Underwater videography as a modern underwater remote sensing tool is a high-resolution coral habitat survey and mapping technique. In this pa…
University of Southern California（南加州大学）；Institute for Creative Technologies（创意技术研究所）；Ubisoft（育碧）；New York University（纽约大学）
Our previous works have demonstrated that visually realistic 3D meshes can be automatically reconstructed with low-cost, off-the-shelf unmanned aerial systems (UAS) equipped with capable cameras, and efficient photogrammetric software techniques. However, such generated data do not contain semantic information/features of objects (i.e., man-made objects, vegetation, ground, object materials, etc.) and cannot allow the sophisticated user-level and system-level interaction. Considering the use case of the data in creating realistic virtual environments for training and simulations (i.e., mission…
M.E. Rinker, Sr. School of Construction Management, University of Florida（佛罗里达大学M.E.林克 Sr.建筑管理学院）；H. Wertheim College of Engineering, University of Florida（佛罗里达大学H.沃特海姆工程学院）
Maintenance of industrial facilities is a growing hazard due to the cumbersome process needed to identify infrastructure degradation. Digital Twins have the potential to improve maintenance by monitoring the continuous digital representation of infrastructure. However, the time needed to map the existing geometry makes their use prohibitive. We previously developed class segmentation algorithms to automate digital twinning, however a vast amount of annotated point clouds is needed. Currently, synthetic data generation for automated segmentation is non-existent. We used Helios++ to automaticall…
University of Southern California（南加州大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；China University of Mining and Technology（中国矿业大学）；Ubisoft（育碧）
In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned aerial vehicles (sUAVs) has provided additional high-resolution imaging capabilities with low cost for mapping a relatively large area of interest. These cutting-edge technologies have caught the US Army and Navy's attention for the purpose of rapid 3D battlefield reconstruction, virtual training, and simulations. Our previous works have demonstrated the importance of information extraction from the derived photogram…
Training point-based deep learning networks for forest segmentation with synthetic data
Instituto en Ciencias de la Computación (UBA-CONICET)（计算机科学研究所（UBA-CONICET））；Departamento de Computación, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires（布宜诺斯艾利斯大学 精确与自然科学学院 计算机系）
National University of Singapore（新加坡国立大学）；State University of New York at Buffalo（纽约州立大学布法罗分校）
Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated and multisource datasets can catalyze the development of those data-driven approaches. To facilitate the research of this area, we present a richly-annotated 3D point cloud dataset for multiple outdoor scene understanding tasks and also an effective learning framework for its hierarchical segmentation task. The dataset was generated via the photogrammetric processing on unmanned aerial vehicle (UAV) images of the National University of Singapore (NUS) campus, …
University of Oxford（牛津大学）；The Hong Kong Polytechnic University（香港理工大学）；Sensat Ltd（Sensat有限公司）；Newcastle University（纽卡斯尔大学）
An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion ri…
CISPA Helmholtz Center for Information Security（CISPA 亥姆霍兹信息安全中心）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
Recently, learning-based image synthesis has enabled to generate high-resolution images, either applying popular adversarial training or a powerful perceptual loss. However, it remains challenging to successfully leverage synthetic data for improving semantic segmentation with additional synthetic images. Therefore, we suggest to generate intermediate convolutional features and propose the first synthesis approach that is catered to such intermediate convolutional features. This allows us to generate new features from label masks and include them successfully into the training procedure in ord…
Robotics and Autonomous Systems The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Data61 Brisbane, Australia（澳大利亚联邦科学与工业研究组织（CSIRO）数据61部布里斯班机器人与自主系统部（Robotics and Autonomous Systems））
Automated segmentation of individual leaves of a plant in an image is a prerequisite to measure more complex phenotypic traits in high-throughput phenotyping. Applying state-of-the-art machine learning approaches to tackle leaf instance segmentation requires a large amount of manually annotated training data. Currently, the benchmark datasets for leaf segmentation contain only a few hundred labeled training images. In this paper, we propose a framework for leaf instance segmentation by augmenting real plant datasets with generated synthetic images of plants inspired by domain randomisation. We…
Recently, increasing attention has been drawn to training semantic segmentation models using synthetic data and computer-generated annotation. However, domain gap remains a major barrier and prevents models learned from synthetic data from generalizing well to real-world applications. In this work, we take the advantage of additional geometric information from synthetic data, a powerful yet largely neglected cue, to bridge the domain gap. Such geometric information can be generated easily from synthetic data, and is proven to be closely coupled with semantic information. With the geometric inf…
Johns Hopkins University（约翰斯·霍普金斯大学）；Zhejiang University（浙江大学）；Beihang University（北京航空航天大学）；Tongji University（同济大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）
Part segmentations provide a rich and detailed part-level description of objects. However, their annotation requires an enormous amount of work, which makes it difficult to apply standard deep learning methods. In this paper, we propose the idea of learning part segmentation through unsupervised domain adaptation (UDA) from synthetic data. We first introduce UDA-Part, a comprehensive part segmentation dataset for vehicles that can serve as an adequate benchmark for UDA (https://qliu24.github.io/udapart). In UDA-Part, we label parts on 3D CAD models which enables us to generate a large set of a…
Indian Institute of Technology (BHU) Varanasi（印度理工学院（BHU）瓦拉纳西分校）；Indian Institute of Technology Gandhinagar（印度理工学院甘地讷格尔分校）；Innit Inc.（Innit公司）
Although Deep Convolutional Neural Networks trained with strong pixel-level annotations have significantly pushed the performance in semantic segmentation, annotation efforts required for the creation of training data remains a roadblock for further improvements. We show that augmentation of the weakly annotated training dataset with synthetic images minimizes both the annotation efforts and also the cost of capturing images with sufficient variety. Evaluation on the PASCAL 2012 validation dataset shows an increase in mean IOU from 52.80% to 55.47% by adding just 100 synthetic images per objec…
Cloud segmentation plays a crucial role in image analysis for climate modeling. Manually labeling the training data for cloud segmentation is time-consuming and error-prone. We explore to train segmentation networks with synthetic data due to the natural acquisition of pixel-level labels. Nevertheless, the domain gap between synthetic and real images significantly degrades the performance of the trained model. We propose a color space adaptation method to bridge the gap, by training a color-sensitive generator and discriminator to adapt synthetic data to real images in color space. Instead of …
University of Catania（卡塔尼亚大学）；Next Vision s.r.l.（Next Vision有限公司）
Being able to understand the relations between the user and the surrounding environment is instrumental to assist users in a worksite. For instance, understanding which objects a user is interacting with from images and video collected through a wearable device can be useful to inform the worker on the usage of specific objects in order to improve productivity and prevent accidents. Despite modern vision systems can rely on advanced algorithms for object detection, semantic and panoptic segmentation, these methods still require large quantities of domain-specific labeled data, which can be dif…
Vanderbilt University（范德堡大学）；Vanderbilt University Medical Center（范德堡大学医学中心）；Incyte Corporation（Incyte公司）
A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality with…
Since annotating pixel-level labels for semantic segmentation is laborious, leveraging synthetic data is an attractive solution. However, due to the domain gap between synthetic domain and real domain, it is challenging for a model trained with synthetic data to generalize to real data. In this paper, considering the fundamental difference between the two domains as the texture, we propose a method to adapt to the texture of the target domain. First, we diversity the texture of synthetic images using a style transfer algorithm. The various textures of generated images prevent a segmentation mo…
Knowledge transfer from synthetic to real data has been widely studied to mitigate data annotation constraints in various computer vision tasks such as semantic segmentation. However, the study focused on 2D images and its counterpart in 3D point clouds segmentation lags far behind due to the lack of large-scale synthetic datasets and effective transfer methods. We address this issue by collecting SynLiDAR, a large-scale synthetic LiDAR dataset that contains point-wise annotated point clouds with accurate geometric shapes and comprehensive semantic classes. SynLiDAR was collected from multiple…
What Makes Synthetic Data Effective in Image Segmentation
State Key Laboratory of Complex and Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beihang University（北京航空航天大学）；School of Computer Science and Engineering（计算机科学与工程学院）
Synthetic Instance Segmentation from Semantic Image Segmentation Masks
Nanjing Forestry University（南京林业大学）；Hong Kong University of Science and Technology（香港科技大学）；Hefei University of Technology（合肥工业大学）；Chinese Academy of Forestry（中国林业科学研究院）
Deep learning has not been routinely employed for semantic segmentation of seabed environment for synthetic aperture sonar (SAS) imagery due to the implicit need of abundant training data such methods necessitate. Abundant training data, specifically pixel-level labels for all images, is usually not available for SAS imagery due to the complex logistics (e.g., diver survey, chase boat, precision position information) needed for obtaining accurate ground-truth. Many hand-crafted feature based algorithms have been proposed to segment SAS in an unsupervised fashion. However, there is still room f…
Training a deep network to perform semantic segmentation requires large amounts of labeled data. To alleviate the manual effort of annotating real images, researchers have investigated the use of synthetic data, which can be labeled automatically. Unfortunately, a network trained on synthetic data performs relatively poorly on real images. While this can be addressed by domain adaptation, existing methods all require having access to real images during training. In this paper, we introduce a drastically different way to handle synthetic images that does not require seeing any real images at tr…
In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. The main problem of UDA for semantic segmentation relies on reducing the domain gap between the real image and synthetic image. To solve this problem, we focused on separating information in an image into content and style. Here, only the content has cues for semantic segmentation, and the style makes the domain gap. Thus, precise separation of content and style in an image leads to effect as supervision of real data even when …
National Taiwan University（台湾大学）；National Tsing Hua University（清华大学）
Anomaly awareness is an essential capability for safety-critical applications such as autonomous driving. While recent progress of robotics and computer vision has enabled anomaly detection for image classification, anomaly detection on semantic segmentation is less explored. Conventional anomaly-aware systems assuming other existing classes as out-of-distribution (pseudo-unknown) classes for training a model will result in two drawbacks. (1) Unknown classes, which applications need to cope with, might not actually exist during training time. (2) Model performance would strongly rely on the cl…
Tree-like structures, such as blood vessels, often express complexity at very fine scales, requiring high-resolution grids to adequately describe their shape. Such sparse morphology can alternately be represented by locations of centreline points, but learning from this type of data with deep learning is challenging due to it being unordered, and permutation invariant. In this work, we propose a deep neural network that directly consumes unordered points along the centreline of a branching structure, to identify the topology of the represented structure in a single-shot. Key to our approach is…
FOSCU: Feasibility of Synthetic MRI Generation via Duo-Diffusion Models for Enhancement of 3D U-Nets in Hepatic Segmentation
Seoul National University（首尔国立大学）；Sangmyung University（祥明大学）；Ewha Womans University（梨花女子大学）；Chung-Ang University（中央大学）；Samsung Medical Center, Sungkyunkwan University School of Medicine（三星医学中心，成均馆大学医学院）；NVIDIA（英伟达）
We show a straightforward and useful methodology for performing instance segmentation using synthetic data. We apply this methodology on a basic case and derived insights through quantitative analysis. We created a new public dataset: The Expo Markers Dataset intended for detection and segmentation tasks. This dataset contains 5,000 synthetic photorealistic images with their corresponding pixel-perfect segmentation ground truth. The goal is to achieve high performance on manually-gathered and annotated real-world data of custom objects. We do that by creating 3D models of the target objects an…
Text-conditioned Segmentation for Tomato Phenotyping via Procedural Synthetic Data
Computer, Electrical and Mathematical Sciences and Engineering Division, KAUST（计算机、电气与数学科学与工程系，沙特阿卜杜拉国王科技大学）；Department of Computer Science, University of Calgary（卡尔加里大学计算机科学系）；Department of Computer Science, Kiel University（基尔大学计算机科学系）；Department of Artificial Intelligence, Adam Mickiewicz University（亚当·密茨凯维奇大学人工智能系）；Department of Electronics and Telecommunications, Polytechnic University of Turin（都灵理工大学电子与电信系）
The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel-level supervisions for a text detection dataset (i.e. where only bounding-box annotations are available) ar…
Pre-training with 3D Synthetic Data: Learning 3D Point Cloud Instance Segmentation from 3D Synthetic Scenes
TICO-AIST Cooperative Research Laboratory for Advanced Logistics (ALlab)（TICO-AIST高级物流合作研究实验室（ALlab））；National Institute of Advanced Industrial Science and Technology (AIST)（日本国立先进工业科学技术研究院（AIST））；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
China Agricultural University（中国农业大学）；University of Bonn（波恩大学）
The mature soybean plants are of complex architecture with pods frequently touching each other, posing a challenge for in-situ segmentation of on-branch soybean pods. Deep learning-based methods can achieve accurate training and strong generalization capabilities, but it demands massive labeled data, which is often a limitation, especially for agricultural applications. As lacking the labeled data to train an in-situ segmentation model for on-branch soybean pods, we propose a transfer learning from synthetic in-vitro soybean pods. First, we present a novel automated image generation method to …
Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios. This is mainly due to two reasons: 1) the model overfits to synthetic images, making the convolutional filters incompetent to extract informative representation for real images; 2) there is a distribution difference between synthetic and real data, which is also known as the domain adaptation problem. To this end, we prop…
In this paper we report on improved part segmentation performance using convolutional neural networks to reduce the dependency on the large amount of manually annotated empirical images. This was achieved by optimising the visual realism of synthetic agricultural images.In Part I, a cycle consistent generative adversarial network was applied to synthetic and empirical images with the objective to generate more realistic synthetic images by translating them to the empirical domain. We first hypothesise and confirm that plant part image features such as color and texture become more similar to t…
