我们描述了一种数据驱动的方法，用于指定任意对象的多个图像，以推断相机观点。该任务是经典几何管道（例如SFM和SLAM）的核心组成部分，也是当代神经方法（例如NERF）的至关重要的预处理要求，以对象重建和视图合成。与现有的对应驱动的方法相反，鉴于稀疏视图的表现不佳，我们提出了一种基于自上而下的预测方法来估计相机观点。我们的主要技术见解是使用基于能量的公式来表示相对摄像机旋转的分布，从而使我们能够明确表示由对象对称或视图引起的多个摄像机模式。利用这些相对预测，我们共同估计了来自多个图像的一致摄像机旋转集。我们表明，我们的方法优于最先进的SFM和SLAM方法，并且在可见和看不见的类别上都稀疏图像。此外，我们的概率方法显着优于直接回归相对姿势的表现，这表明对多模型建模对于相干关节重建很重要。我们证明，我们的系统可以是从多视图数据集中进行野外重建的垫脚石。可以在https://jasonyzhang.com/relpose上找到带有代码和视频的项目页面。

Camera pose estimation is a key step in standard 3D reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation often fail when only a few images are available because they rely on the ability to robustly identify and match visual features between image pairs. While these methods can work robustly with dense camera views, capturing a large set of images can be time-consuming or impractical. We propose SparsePose for recovering accurate camera poses given a sparse set of wide-baseline images (fewer than 10). The method learns to regress initial camera poses and then iteratively refine them after training on a large-scale dataset of objects (Co3D: Common Objects in 3D). SparsePose significantly outperforms conventional and learning-based baselines in recovering accurate camera rotations and translations. We also demonstrate our pipeline for high-fidelity 3D reconstruction using only 5-9 images of an object.

We introduce an approach for recovering the 6D pose of multiple known objects in a scene captured by a set of input images with unknown camera viewpoints. First, we present a single-view single-object 6D pose estimation method, which we use to generate 6D object pose hypotheses. Second, we develop a robust method for matching individual 6D object pose hypotheses across different input images in order to jointly estimate camera viewpoints and 6D poses of all objects in a single consistent scene. Our approach explicitly handles object symmetries, does not require depth measurements, is robust to missing or incorrect object hypotheses, and automatically recovers the number of objects in the scene. Third, we develop a method for global scene refinement given multiple object hypotheses and their correspondences across views. This is achieved by solving an object-level bundle adjustment problem that refines the poses of cameras and objects to minimize the reprojection error in all views. We demonstrate that the proposed method, dubbed Cosy-Pose, outperforms current state-of-the-art results for single-view and multi-view 6D object pose estimation by a large margin on two challenging benchmarks: the YCB-Video and T-LESS datasets. Code and pre-trained models are available on the project webpage. 5

单图像姿势估计是许多视觉和机器人任务中的一个基本问题，并且现有的深度学习方法不会完全建模和处理来遭受：i）关于预测的不确定性，ii）具有多个（有时是无限）正确姿势的对称对象。为此，我们引入了一种在SO（3）上估算任意非参数分布的方法。我们的关键思想是通过神经网络隐含地表示分布，该神经网络估计给定输入图像和候选姿势的概率。网格采样或梯度上升可用于找到最有可能的姿势，但也可以评估任何姿势的概率，从而实现关于对称性和不确定性的推理。这是代表流形分布的最通用方法，为了展示丰富的表现力，我们介绍了一个具有挑战性的对称和几乎对称对象的数据集。我们不需要对姿势不确定性的监督 - 模型仅以一个示例训练单个姿势。但是，我们的隐式模型具有高度表达能力在3D姿势上处理复杂的分布，同时仍然在标准的非歧义环境上获得准确的姿势估计，从而在Pascal3d+和ModelNet10-SO-SO（3）基准方面实现了最先进的性能。

While object reconstruction has made great strides in recent years, current methods typically require densely captured images and/or known camera poses, and generalize poorly to novel object categories. To step toward object reconstruction in the wild, this work explores reconstructing general real-world objects from a few images without known camera poses or object categories. The crux of our work is solving two fundamental 3D vision problems -- shape reconstruction and pose estimation -- in a unified approach. Our approach captures the synergies of these two problems: reliable camera pose estimation gives rise to accurate shape reconstruction, and the accurate reconstruction, in turn, induces robust correspondence between different views and facilitates pose estimation. Our method FORGE predicts 3D features from each view and leverages them in conjunction with the input images to establish cross-view correspondence for estimating relative camera poses. The 3D features are then transformed by the estimated poses into a shared space and are fused into a neural radiance field. The reconstruction results are rendered by volume rendering techniques, enabling us to train the model without 3D shape ground-truth. Our experiments show that FORGE reliably reconstructs objects from five views. Our pose estimation method outperforms existing ones by a large margin. The reconstruction results under predicted poses are comparable to the ones using ground-truth poses. The performance on novel testing categories matches the results on categories seen during training. Project page: https://ut-austin-rpl.github.io/FORGE/

学习3D对象类别的传统方法使用合成数据或手动监控。在本文中，我们提出了一种不需要手动注释的方法，而是通过观察来自移动的有利点的物体来阐述。我们的系统在两种创新上构建：暹罗视点分解网络，不太明确地比较3D形状，强大地对准不同的视频;和3D形状完成网络可以从部分观察中提取对象的完整形状。我们还展示了配置网络来执行概率预测以及几何感知数据增强方案的好处。我们在公开可用的基准上获得最先进的结果。

We present ObjectMatch, a semantic and object-centric camera pose estimation for RGB-D SLAM pipelines. Modern camera pose estimators rely on direct correspondences of overlapping regions between frames; however, they cannot align camera frames with little or no overlap. In this work, we propose to leverage indirect correspondences obtained via semantic object identification. For instance, when an object is seen from the front in one frame and from the back in another frame, we can provide additional pose constraints through canonical object correspondences. We first propose a neural network to predict such correspondences on a per-pixel level, which we then combine in our energy formulation with state-of-the-art keypoint matching solved with a joint Gauss-Newton optimization. In a pairwise setting, our method improves registration recall of state-of-the-art feature matching from 77% to 87% overall and from 21% to 52% in pairs with 10% or less inter-frame overlap. In registering RGB-D sequences, our method outperforms cutting-edge SLAM baselines in challenging, low frame-rate scenarios, achieving more than 35% reduction in trajectory error in multiple scenes.

我们提出了一种从有限重叠的图像中对场景进行平面表面重建的方法。此重构任务是具有挑战性的，因为它需要共同推理单个图像3D重建，图像之间的对应关系以及图像之间的相对摄像头姿势。过去的工作提出了基于优化的方法。我们引入了一种更简单的方法，即平面形式，该方法使用应用于3D感知平面令牌的变压器执行3D推理。我们的实验表明，我们的方法比以前的工作更有效，并且几项3D特定的设计决策对于成功的成功至关重要。

We introduce ViewNeRF, a Neural Radiance Field-based viewpoint estimation method that learns to predict category-level viewpoints directly from images during training. While NeRF is usually trained with ground-truth camera poses, multiple extensions have been proposed to reduce the need for this expensive supervision. Nonetheless, most of these methods still struggle in complex settings with large camera movements, and are restricted to single scenes, i.e. they cannot be trained on a collection of scenes depicting the same object category. To address these issues, our method uses an analysis by synthesis approach, combining a conditional NeRF with a viewpoint predictor and a scene encoder in order to produce self-supervised reconstructions for whole object categories. Rather than focusing on high fidelity reconstruction, we target efficient and accurate viewpoint prediction in complex scenarios, e.g. 360{\deg} rotation on real data. Our model shows competitive results on synthetic and real datasets, both for single scenes and multi-instance collections.

铰接式3D形状重建的事先工作通常依赖于专用传感器（例如，同步的多摄像机系统）或预先构建的3D可变形模型（例如，Smal或SMPL）。这些方法无法在野外扩展到不同的各种物体。我们呈现Banmo，这是一种需要专用传感器的方法，也不需要预定义的模板形状。 Banmo在可怜的渲染框架中从许多单眼休闲视频中建立高保真，铰接式的3D模型（包括形状和动画皮肤的重量）。虽然许多视频的使用提供了更多的相机视图和对象关节的覆盖范围，但它们在建立不同背景，照明条件等方面建立了重大挑战。我们的主要洞察力是合并三所思想学校; （1）使用铰接骨骼和混合皮肤的经典可变形形状模型，（2）可容纳基于梯度的优化，（3）在像素之间产生对应关系的规范嵌入物模型。我们介绍了神经混合皮肤模型，可允许可微分和可逆的铰接变形。与规范嵌入式结合时，这些模型允许我们在跨越可通过循环一致性自我监督的视频中建立密集的对应。在真实和合成的数据集上，Banmo显示比人类和动物的先前工作更高保真3D重建，具有从新颖的观点和姿势的现实图像。项目网页：Banmo-www.github.io。

从极端视图图像中恢复相机的空间布局和场景的几何形状是计算机视觉的长期挑战。盛行的3D重建算法通常采用匹配范式的图像，并假定场景的一部分是可以在图像上进行的，当输入之间几乎没有重叠时的性能较差。相比之下，人类可以通过形状的先验知识将一个图像中的可见部分与另一个图像中相应的不可见组件相关联。受这个事实的启发，我们提出了一个名为虚拟通信（VC）的新颖概念。 VC是来自两个图像的一对像素，它们的相机射线在3D中相交。与经典的对应关系相似，VC符合异性几何形状；与经典的信件不同，VC不需要在视图上可以共同提供。因此，即使图像不重叠，也可以建立和利用VC。我们介绍了一种方法，可以在场景中找到基于人类的虚拟对应关系。我们展示了如何与经典捆绑捆绑调整无缝集成的风险投资，以恢复跨极视图的相机姿势。实验表明，在具有挑战性的情况下，我们的方法显着优于最先进的摄像头姿势估计方法，并且在传统的密集捕获的设置中是可比的。我们的方法还释放了多个下游任务的潜力，例如在极端视图场景中从多视图立体声和新型视图合成中进行场景重建。

我们提出了一个简单的基线，用于直接估计两个图像之间的相对姿势（旋转和翻译，包括比例）。深度方法最近显示出很强的进步，但通常需要复杂或多阶段的体系结构。我们表明，可以将少数修改应用于视觉变压器（VIT），以使其计算接近八点算法。这种归纳偏见使一种简单的方法在多种环境中具有竞争力，通常在有限的数据制度中具有强劲的性能增长，从而实质上有所改善。

We introduce MegaPose, a method to estimate the 6D pose of novel objects, that is, objects unseen during training. At inference time, the method only assumes knowledge of (i) a region of interest displaying the object in the image and (ii) a CAD model of the observed object. The contributions of this work are threefold. First, we present a 6D pose refiner based on a render&compare strategy which can be applied to novel objects. The shape and coordinate system of the novel object are provided as inputs to the network by rendering multiple synthetic views of the object's CAD model. Second, we introduce a novel approach for coarse pose estimation which leverages a network trained to classify whether the pose error between a synthetic rendering and an observed image of the same object can be corrected by the refiner. Third, we introduce a large-scale synthetic dataset of photorealistic images of thousands of objects with diverse visual and shape properties and show that this diversity is crucial to obtain good generalization performance on novel objects. We train our approach on this large synthetic dataset and apply it without retraining to hundreds of novel objects in real images from several pose estimation benchmarks. Our approach achieves state-of-the-art performance on the ModelNet and YCB-Video datasets. An extensive evaluation on the 7 core datasets of the BOP challenge demonstrates that our approach achieves performance competitive with existing approaches that require access to the target objects during training. Code, dataset and trained models are available on the project page: https://megapose6d.github.io/.

We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but fail to generate unseen regions or handle uncertainty under large viewpoint changes. Alternate methods treat this as a (probabilistic) 2D synthesis task, and while they can generate plausible 2D images, they do not infer a consistent underlying 3D. However, we find that this trade-off between 3D consistency and probabilistic image generation does not need to exist. In fact, we show that geometric consistency and generative inference can be complementary in a mode-seeking behavior. By distilling a 3D consistent scene representation from a view-conditioned latent diffusion model, we are able to recover a plausible 3D representation whose renderings are both accurate and realistic. We evaluate our approach across 51 categories in the CO3D dataset and show that it outperforms existing methods, in both distortion and perception metrics, for sparse-view novel view synthesis.

Video provides us with the spatio-temporal consistency needed for visual learning. Recent approaches have utilized this signal to learn correspondence estimation from close-by frame pairs. However, by only relying on close-by frame pairs, those approaches miss out on the richer long-range consistency between distant overlapping frames. To address this, we propose a self-supervised approach for correspondence estimation that learns from multiview consistency in short RGB-D video sequences. Our approach combines pairwise correspondence estimation and registration with a novel SE(3) transformation synchronization algorithm. Our key insight is that self-supervised multiview registration allows us to obtain correspondences over longer time frames; increasing both the diversity and difficulty of sampled pairs. We evaluate our approach on indoor scenes for correspondence estimation and RGB-D pointcloud registration and find that we perform on-par with supervised approaches.

Understanding the 3D world without supervision is currently a major challenge in computer vision as the annotations required to supervise deep networks for tasks in this domain are expensive to obtain on a large scale. In this paper, we address the problem of unsupervised viewpoint estimation. We formulate this as a self-supervised learning task, where image reconstruction provides the supervision needed to predict the camera viewpoint. Specifically, we make use of pairs of images of the same object at training time, from unknown viewpoints, to self-supervise training by combining the viewpoint information from one image with the appearance information from the other. We demonstrate that using a perspective spatial transformer allows efficient viewpoint learning, outperforming existing unsupervised approaches on synthetic data, and obtains competitive results on the challenging PASCAL3D+ dataset.

从2D图像中学习可变形的3D对象通常是一个不适的问题。现有方法依赖于明确的监督来建立多视图对应关系，例如模板形状模型和关键点注释，这将其在“野外”中的对象上限制了。建立对应关系的一种更自然的方法是观看四处移动的对象的视频。在本文中，我们介绍了Dove，一种方法，可以从在线可用的单眼视频中学习纹理的3D模型，而无需关键点，视点或模板形状监督。通过解决对称性诱导的姿势歧义并利用视频中的时间对应关系，该模型会自动学会从每个单独的RGB框架中分解3D形状，表达姿势和纹理，并准备在测试时间进行单像推断。在实验中，我们表明现有方法无法学习明智的3D形状，而无需其他关键点或模板监督，而我们的方法在时间上产生了时间一致的3D模型，可以从任意角度来对其进行动画和呈现。

机器人操纵可以配制成诱导一系列空间位移：其中移动的空间可以包括物体，物体的一部分或末端执行器。在这项工作中，我们提出了一个简单的模型架构，它重新排列了深度功能，以从视觉输入推断出可视输入的空间位移 - 这可以参数化机器人操作。它没有对象的假设（例如规范姿势，模型或关键点），它利用空间对称性，并且比我们学习基于视觉的操纵任务的基准替代方案更高的样本效率，并且依赖于堆叠的金字塔用看不见的物体组装套件;从操纵可变形的绳索，以将堆积的小物体推动，具有闭环反馈。我们的方法可以表示复杂的多模态策略分布，并推广到多步顺序任务，以及6dof拾取器。 10个模拟任务的实验表明，它比各种端到端基线更快地学习并概括，包括使用地面真实对象姿势的政策。我们在现实世界中使用硬件验证我们的方法。实验视频和代码可在https://transporternets.github.io获得

我们介绍了日常桌面对象的998 3D型号的数据集及其847,000个现实世界RGB和深度图像。每个图像的相机姿势和对象姿势的准确注释都以半自动化方式执行，以促进将数据集用于多种3D应用程序，例如形状重建，对象姿势估计，形状检索等。3D重建由于缺乏适当的现实世界基准来完成该任务，并证明我们的数据集可以填补该空白。整个注释数据集以及注释工具和评估基线的源代码可在http://www.ocrtoc.org/3d-reconstruction.html上获得。