The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

对异常域特定视频集的有效分析是一个重要的实践问题，在该问题中，最新的通用模型仍面临局限性。因此，希望设计基准数据集，以挑战具有其他约束的特定领域的新型强大模型。重要的是要记住，特定域的数据可能更嘈杂（例如，内窥镜或水下视频），并且通常需要更多经验丰富的用户才能有效搜索。在本文中，我们专注于从水下环境中移动相机拍摄的单次视频，这构成了研究目的的非平凡挑战。提出了新的海洋视频套件数据集的第一个碎片，用于用于视频检索和其他计算机视觉挑战。除了基本的元数据统计数据外，我们还基于低级特征以及所选密钥帧的语义注释提供了几个见解和参考图。该分析还包含实验，显示了检索受人尊敬的通用模型的局限性。

建筑摄影是一种摄影类型，重点是捕获前景中带有戏剧性照明的建筑物或结构。受图像到图像翻译方法的成功启发，我们旨在为建筑照片执行风格转移。但是，建筑摄影中的特殊构图对这类照片中的样式转移构成了巨大挑战。现有的神经风格转移方法将建筑图像视为单个实体，它将产生与原始建筑的几何特征，产生不切实际的照明，错误的颜色演绎以及可视化伪影，例如幽灵，外观失真或颜色不匹配。在本文中，我们专门针对建筑摄影的神经风格转移方法。我们的方法解决了两个分支神经网络中建筑照片中前景和背景的组成，该神经网络分别考虑了前景和背景的样式转移。我们的方法包括一个分割模块，基于学习的图像到图像翻译模块和图像混合优化模块。我们使用了一天中不同的魔术时代捕获的不受限制的户外建筑照片的新数据集培训了图像到图像的翻译神经网络，利用其他语义信息，以更好地匹配和几何形状保存。我们的实验表明，我们的方法可以在前景和背景上产生逼真的照明和颜色演绎，并且在定量和定性上都优于一般图像到图像转换和任意样式转移基线。我们的代码和数据可在https://github.com/hkust-vgd/architectural_style_transfer上获得。

神经辐射场（NERF）最近在新型视图合成中取得了令人印象深刻的结果。但是，以前的NERF作品主要关注以对象为中心的方案。在这项工作中，我们提出了360ROAM，这是一种新颖的场景级NERF系统，可以实时合成大型室内场景的图像并支持VR漫游。我们的系统首先从多个输入$ 360^\ circ $图像构建全向神经辐射场360NERF。然后，我们逐步估算一个3D概率的占用图，该概率占用图代表了空间密度形式的场景几何形状。跳过空的空间和上采样占据的体素本质上可以使我们通过以几何学意识的方式使用360NERF加速量渲染。此外，我们使用自适应划分和扭曲策略来减少和调整辐射场，以进一步改进。从占用地图中提取的场景的平面图可以为射线采样提供指导，并促进现实的漫游体验。为了显示我们系统的功效，我们在各种场景中收集了$ 360^\ Circ $图像数据集并进行广泛的实验。基线之间的定量和定性比较说明了我们在复杂室内场景的新型视图合成中的主要表现。

来自3D点云的对象重建在计算机视觉和计算机图形研究字段中取得了令人印象深刻的进展。但是，通常会忽略时间变化点云（又称4D点云）的重建。在本文中，我们提出了一种新的网络体系结构，即RFNET-4D，它共同重建对象及其运动从4D点云中流动。关键见解是，通过一系列点云的学习空间和时间特征同时执行这两个任务可以利用单个任务，从而改善了整体性能。为了证明这种能力，我们使用无监督的学习方法来设计一个时间矢量场学习模块，以进行流程估计，并通过监督对物体重建的空间结构的监督学习来利用。基准数据集的广泛实验和分析验证了我们方法的有效性和效率。如实验结果所示，我们的方法在流动估计和对象重建方面都达到了最先进的性能，同时执行训练和推理中的现有方法要快得多。我们的代码和数据可从https://github.com/hkust-vgd/rfnet-4d获得

草图和照片之间的巨大领域差距以及高度抽象的草图表示构成了基于草图的图像检索（\下划线{Sbir}）的挑战。基于零拍的草图的图像检索（\下划线{ZS-SBIR}）更通用，实用，但由于所看到和未遵守的类别之间的额外知识差距，造成更大的挑战。要同时缓解两个间隙，我们提出了一个\ textbf {a} pproaching-and-\ textbf {c}映射\ textbf {net}工作（称为`\ textbf {acnet}''），以共同优化素描到照片合成与图像检索。检索模块引导综合模块生成大量不同的光相似图像，该图像逐渐接近照片域，从而更好地服务于检索模块，而不是以前学习域名不可知的表征和类别 - 无名的共同知识，以概括到未经证明的类别。通过检索引导产生的这些不同的图像可以有效地减轻了高梯度的混凝土类别训练样本的过度装备问题。我们还发现使用基于代理的NormsoftMax丢失是有效的，因为它的集中效果可以稳定我们的联合培训并促进未经看管分类的概括能力。我们的方法简单而且有效，这在两个广泛使用的ZS-SBIR数据集上实现了最先进的性能，并通过大边距超过以前的方法。

提供和渲染室内场景一直是室内设计的一项长期任务，艺术家为空间创建概念设计，建立3D模型的空间，装饰，然后执行渲染。尽管任务很重要，但它很乏味，需要巨大的努力。在本文中，我们引入了一个特定领域的室内场景图像合成的新问题，即神经场景装饰。鉴于一张空的室内空间的照片以及用户确定的布局列表，我们旨在合成具有所需的家具和装饰的相同空间的新图像。神经场景装饰可用于以简单而有效的方式创建概念室内设计。我们解决这个研究问题的尝试是一种新颖的场景生成体系结构，它将空的场景和对象布局转化为现实的场景照片。我们通过将其与有条件图像合成基线进行比较，以定性和定量的方式将其进行比较，证明了我们提出的方法的性能。我们进行广泛的实验，以进一步验证我们生成的场景的合理性和美学。我们的实现可在\ url {https://github.com/hkust-vgd/neural_scene_decoration}获得。

Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with high accuracy (∼92%). Despite such impressive results, in this paper, we argue that object classification is still a challenging task when objects are framed with real-world settings. To prove this, we introduce ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data. From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions. We identify three key open problems for point cloud object classification, and propose new point cloud classification neural networks that achieve state-of-the-art performance on classifying objects with cluttered background. Our dataset and code are publicly available in our project page 1 .

The task of reconstructing 3D human motion has wideranging applications. The gold standard Motion capture (MoCap) systems are accurate but inaccessible to the general public due to their cost, hardware and space constraints. In contrast, monocular human mesh recovery (HMR) methods are much more accessible than MoCap as they take single-view videos as inputs. Replacing the multi-view Mo- Cap systems with a monocular HMR method would break the current barriers to collecting accurate 3D motion thus making exciting applications like motion analysis and motiondriven animation accessible to the general public. However, performance of existing HMR methods degrade when the video contains challenging and dynamic motion that is not in existing MoCap datasets used for training. This reduces its appeal as dynamic motion is frequently the target in 3D motion recovery in the aforementioned applications. Our study aims to bridge the gap between monocular HMR and multi-view MoCap systems by leveraging information shared across multiple video instances of the same action. We introduce the Neural Motion (NeMo) field. It is optimized to represent the underlying 3D motions across a set of videos of the same action. Empirically, we show that NeMo can recover 3D motion in sports using videos from the Penn Action dataset, where NeMo outperforms existing HMR methods in terms of 2D keypoint detection. To further validate NeMo using 3D metrics, we collected a small MoCap dataset mimicking actions in Penn Action,and show that NeMo achieves better 3D reconstruction compared to various baselines.

This work builds on the models and concepts presented in part 1 to learn approximate dictionary representations of Koopman operators from data. Part I of this paper presented a methodology for arguing the subspace invariance of a Koopman dictionary. This methodology was demonstrated on the state-inclusive logistic lifting (SILL) basis. This is an affine basis augmented with conjunctive logistic functions. The SILL dictionary's nonlinear functions are homogeneous, a norm in data-driven dictionary learning of Koopman operators. In this paper, we discover that structured mixing of heterogeneous dictionary functions drawn from different classes of nonlinear functions achieve the same accuracy and dimensional scaling as the deep-learning-based deepDMD algorithm. We specifically show this by building a heterogeneous dictionary comprised of SILL functions and conjunctive radial basis functions (RBFs). This mixed dictionary achieves the same accuracy and dimensional scaling as deepDMD with an order of magnitude reduction in parameters, while maintaining geometric interpretability. These results strengthen the viability of dictionary-based Koopman models to solving high-dimensional nonlinear learning problems.