In this paper, we introduce neural texture learning for 6D object pose estimation from synthetic data and a few unlabelled real images. Our major contribution is a novel learning scheme which removes the drawbacks of previous works, namely the strong dependency on co-modalities or additional refinement. These have been previously necessary to provide training signals for convergence. We formulate such a scheme as two sub-optimisation problems on texture learning and pose learning. We separately learn to predict realistic texture of objects from real image collections and learn pose estimation from pixel-perfect synthetic data. Combining these two capabilities allows then to synthesise photorealistic novel views to supervise the pose estimator with accurate geometry. To alleviate pose noise and segmentation imperfection present during the texture learning phase, we propose a surfel-based adversarial training loss together with texture regularisation from synthetic data. We demonstrate that the proposed approach significantly outperforms the recent state-of-the-art methods without ground-truth pose annotations and demonstrates substantial generalisation improvements towards unseen scenes. Remarkably, our scheme improves the adopted pose estimators substantially even when initialised with much inferior performance.

Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it difficult to detect every possible object. While deep learning approaches may be effective for this problem, manually annotating 3D data for supervised learning is time-consuming. In this work, we explore zero-shot instance segmentation (ZSIS) from RGB-D data to identify unseen objects in a semantic category-agnostic manner. We introduce a zero-shot split for Tabletop Objects Dataset (TOD-Z) to enable this study and present a method that uses annotated objects to learn the ``objectness'' of pixels and generalize to unseen object categories in cluttered indoor environments. Our method, SupeRGB-D, groups pixels into small patches based on geometric cues and learns to merge the patches in a deep agglomerative clustering fashion. SupeRGB-D outperforms existing baselines on unseen objects while achieving similar performance on seen objects. Additionally, it is extremely lightweight (0.4 MB memory requirement) and suitable for mobile and robotic applications. The dataset split and code will be made publicly available upon acceptance.

Estimating the 6D pose of objects is one of the major fields in 3D computer vision. Since the promising outcomes from instance-level pose estimation, the research trends are heading towards category-level pose estimation for more practical application scenarios. However, unlike well-established instance-level pose datasets, available category-level datasets lack annotation quality and provided pose quantity. We propose the new category level 6D pose dataset HouseCat6D featuring 1) Multi-modality of Polarimetric RGB+P and Depth, 2) Highly diverse 194 objects of 10 household object categories including 2 photometrically challenging categories, 3) High-quality pose annotation with an error range of only 1.35 mm to 1.74 mm, 4) 41 large scale scenes with extensive viewpoint coverage, 5) Checkerboard-free environment throughout the entire scene. We also provide benchmark results of state-of-the-art category-level pose estimation networks.

Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or the edges in the graph. Although existing works have shown promising results in modifying the placement and pose of objects, scene manipulation often leads to losing some visual characteristics like the appearance or identity of objects. In this work, we propose DisPositioNet, a model that learns a disentangled representation for each object for the task of image manipulation using scene graphs in a self-supervised manner. Our framework enables the disentanglement of the variational latent embeddings as well as the feature representation in the graph. In addition to producing more realistic images due to the decomposition of features like pose and identity, our method takes advantage of the probabilistic sampling in the intermediate features to generate more diverse images in object replacement or addition tasks. The results of our experiments show that disentangling the feature representations in the latent manifold of the model outperforms the previous works qualitatively and quantitatively on two public benchmarks. Project Page: https://scenegenie.github.io/DispositioNet/

Data augmentation is a valuable tool for the design of deep learning systems to overcome data limitations and stabilize the training process. Especially in the medical domain, where the collection of large-scale data sets is challenging and expensive due to limited access to patient data, relevant environments, as well as strict regulations, community-curated large-scale public datasets, pretrained models, and advanced data augmentation methods are the main factors for developing reliable systems to improve patient care. However, for the development of medical acoustic sensing systems, an emerging field of research, the community lacks large-scale publicly available data sets and pretrained models. To address the problem of limited data, we propose a conditional generative adversarial neural network-based augmentation method which is able to synthesize mel spectrograms from a learned data distribution of a source data set. In contrast to previously proposed fully convolutional models, the proposed model implements residual Squeeze and Excitation modules in the generator architecture. We show that our method outperforms all classical audio augmentation techniques and previously published generative methods in terms of generated sample quality and a performance improvement of 2.84% of Macro F1-Score for a classifier trained on the augmented data set, an enhancement of $1.14\%$ in relation to previous work. By analyzing the correlation of intermediate feature spaces, we show that the residual Squeeze and Excitation modules help the model to reduce redundancy in the latent features. Therefore, the proposed model advances the state-of-the-art in the augmentation of clinical audio data and improves the data bottleneck for the design of clinical acoustic sensing systems.

6多机器人抓钩是一个持久但未解决的问题。最近的方法利用强3D网络从深度传感器中提取几何抓握表示形式，表明对公共物体的准确性卓越，但对光度化挑战性物体（例如，透明或反射材料中的物体）进行不满意。瓶颈在于这些物体的表面由于光吸收或折射而无法反射准确的深度。在本文中，与利用不准确的深度数据相反，我们提出了第一个称为MonograspNet的只有RGB的6-DOF握把管道，该管道使用稳定的2D特征同时处理任意对象抓握，并克服由光学上具有挑战性挑战的对象引起的问题。 MonograspNet利用关键点热图和正常地图来恢复由我们的新型表示形式表示的6-DOF抓握姿势，该表示的2D键盘具有相应的深度，握把方向，抓握宽度和角度。在真实场景中进行的广泛实验表明，我们的方法可以通过在抓住光学方面挑战的对象方面抓住大量对象并超过基于深度的竞争者的竞争成果。为了进一步刺激机器人的操纵研究，我们还注释并开源一个多视图和多场景现实世界抓地数据集，其中包含120个具有20m精确握把标签的混合光度复杂性对象。

由于全景分割为输入中的每个像素提供了一个预测，因此，非标准和看不见的对象系统地导致了错误的输出。但是，在关键的环境中，针对分发样本的鲁棒性和角案件对于避免危险行为至关重要，例如忽略动物或道路上的货物丢失。由于驾驶数据集不能包含足够的数据点来正确采样基础分布的长尾巴，因此方法必须处理未知和看不见的方案才能安全部署。以前的方法是通过重新识别已经看到未标记的对象来针对此问题的一部分。在这项工作中，我们扩大了提出整体分割的范围：一项任务，以识别和将看不见的对象分为实例，而无需从未知数中学习，同时执行已知类别的全面分割。我们用U3HS解决了这个新问题，U3HS首先将未知数视为高度不确定的区域，然后将相应的实例感知嵌入到各个对象中。通过这样做，这是第一次使用未知对象进行综合分割，我们的U3HS未接受未知数据的训练，因此使对象类型的设置不受限制，并允许对整体场景理解。在两个公共数据集上进行了广泛的实验和比较，即CityScapes和作为转移的丢失和发现，证明了U3HS在挑战性的整体分段任务中的有效性，并具有竞争性的封闭式全盘分段性能。

使用增强现实（AR）用于导航目的，这表明在手术手术过程中协助医生有益。这些应用通常需要知道外科手术工具和患者的姿势，以提供外科医生在任务执行过程中可以使用的视觉信息。现有的医学级跟踪系统使用放置在手术室内的红外摄像头（OR）来识别感兴趣的对象附加并计算其姿势的复古反射标记。一些市售的AR头式显示器（HMD）使用类似的摄像头进行自定位，手动跟踪和估算对象的深度。这项工作提出了一个使用AR HMD的内置摄像机来准确跟踪复古反射标记的框架，例如在手术过程中使用的标记，而无需集成任何其他组件。该框架还能够同时跟踪多个工具。我们的结果表明，横向翻译的准确度为0.09 +-0.06毫米，可以实现标记的跟踪和检测，纵向翻译的0.42 +-0.32 mm，绕垂直轴旋转的0.80 +-0.39 ver。此外，为了展示所提出的框架的相关性，我们在手术程序的背景下评估了系统的性能。该用例旨在在骨科过程中复制K-Wire插入的场景。为了进行评估，为两名外科医生和一名生物医学研究人员提供了视觉导航，每次都进行了21次注射。该用例的结果提供了与基于AR的导航程序报告的相当精度。

机器人超声（US）成像旨在克服美国自由企业考试的一些局限性，例如难以保证操作员可重复性。然而，由于患者之间的解剖学和生理变化以及解剖下结构的相对运动，富有鲁棒性产生最佳轨迹以检查感兴趣的解剖学时，当他们构成明确的关节时，这是一项挑战。为了应对这一挑战，本文提出了一种基于视觉的方法，允许自动机器人美国肢体扫描。为此，使用带注释的血管结构的人臂的Atlas MRI模板用于生成轨迹并注册并将其投射到患者的皮肤表面上，以进行机器人的美国获得。为了有效地细分并准确地重建目标的3D容器，我们通过将通道注意模块纳入U-NET型神经网络中，利用连续美国框架中的空间连续性。自动轨迹生成方法对具有各种铰接关节角度的六名志愿者进行评估。在所有情况下，该系统都可以成功地获取志愿者四肢上计划的血管结构。对于一名志愿者，还提供了MRI扫描，可以评估美国图像中扫描动脉的平均半径，从而导致半径估计（$ 1.2 \ pm0.05〜mm $）可与MRI地面真相相当（$ 1.2 \ $ $） PM0.04〜mm $）。

机器人超声（US）成像已被视为克服美国自由手检查的局限性，即操作员互操作机构的局限性。 \修订{然而，机器人美国系统在扫描过程中无法对主体运动做出反应，这限制了他们的临床接受。}关于人类超声检查员，他们经常通过重新定位探针甚至重新启动摄取，尤其是因为扫描而对患者的运动做出反应。具有较长结构等肢体动脉的解剖学。为了实现这一特征，我们提出了一个基于视觉的系统来监视受试者的运动并自动更新扫描轨迹，从而无缝获得目标解剖结构的完整3D图像。使用RGB图像中的分段对象掩码开发运动监视模块。一旦受试者移动，机器人将通过使用迭代最接近点算法在移动前后获得的对象的表面点云来停止并重新计算合适的轨迹。之后，为了确保重新定位US探针后的最佳接触条件，使用基于置信的微调过程来避免探针和接触表面之间的潜在间隙。最后，整个系统在具有不均匀表面的人类臂幻象上进行了验证，而对象分割网络也在志愿者上得到验证。结果表明，提出的系统可以对对象运动做出反应，并可靠地提供准确的3D图像。