本文介绍了一种数据驱动的形状完成方法，该方法着重于完成3D形状缺失区域的几何细节。我们观察到，现有的生成方法缺乏训练数据和表示能力，可以通过复杂的几何形状和拓扑合成合理的，细粒度的细节。我们的关键见解是从部分输入复制和变形补丁以完成缺失区域。这使我们能够保留本地几何特征的风格，即使它与培训数据有很大不同。我们的全自动方法分为两个阶段。首先，我们学会从输入形状检索候选补丁。其次，我们选择并变形了一些检索到的候选者，以无缝将它们融合到完整的形状中。该方法结合了两种最常见的完成方法的优点：基于相似性的单稳定性完成，以及通过学习形状空间来完成。我们通过从部分输入中检索贴片来利用重复模式，并通过使用神经网络来指导检索和变形步骤来学习全球结构先验。实验结果表明，我们的方法在多个数据集和形状类别上的表现非常优于基线。代码和数据可在https://github.com/gitbosun/patchrd上找到。

重建3D对象是重要的计算机视觉任务，在AR/VR中具有广泛的应用。为此任务开发的深度学习算法通常依赖于不切实际的合成数据集，例如shapenet和things3d。另一方面，现有的以对象为中心的数据集通常没有足够的注释来实现监督培训或可靠的评估。在此技术报告中，我们提出了一个以照片为中心的对象数据集HM3D-ABO。它是通过构成现实的室内场景和现实对象来构建的。对于每种配置，我们提供多视图RGB观测值，这是对象，地面真实深度图和对象掩码的水密网格模型。所提出的数据集也可用于诸如摄像头估计和新颖视图合成之类的任务。数据集生成代码在https://github.com/zhenpeiyang/hm3d-abo上发布。

许多3D表示（例如，点云）是下面连续3D表面的离散样本。该过程不可避免地介绍了底层的3D形状上的采样变化。在学习3D表示中，应忽略应忽略变化，而应捕获基础3D形状的可转换知识。这成为现有代表学习范式的大挑战。本文在点云上自动编码。标准自动编码范例强制编码器捕获这种采样变体，因为解码器必须重建具有采样变化的原始点云。我们介绍了隐式AutoEncoder（IAE），这是一种简单而有效的方法，通过用隐式解码器替换点云解码器来解决这一挑战。隐式解码器输出与相同模型的不同点云采样之间共享的连续表示。在隐式表示下重建可以优先考虑编码器丢弃采样变体，引入更多空间以学习有用的功能。在一个简单的线性AutoEncoder下，理论上理论地证明这一索赔。此外，隐式解码器提供丰富的空间来为不同的任务设计合适的隐式表示。我们展示了IAE对3D对象和3D场景的各种自我监督学习任务的有用性。实验结果表明，IAE在每项任务中始终如一地优于最先进的。

开发深度神经网络以生成3D场景是神经综合的基本问题，其立即应用于架构CAD，计算机图形，以及生成虚拟机器人训练环境。这项任务是具有挑战性的，因为3D场景呈现不同的模式，从连续的模式等等，例如对象尺寸和成对对之间的相对姿势，以离散模式，例如具有对称关系的对象的发生和共发生。本文介绍了一种新型神经场景综合方法，可以捕获3D场景的不同特征模式。我们的方法结合了神经网络和传统场景合成方法的强度。我们使用从训练数据中学到的参数上的分布，这提供了对象属性和相对属性的不确定性，以规范前馈神经模型的输出。此外，我们的方法不仅仅是预测场景布局，而不是预测场景布局。该方法允许我们利用预测属性之间的底层一致性约束来修剪不可行的预测。实验结果表明，我们的方法显着优于现有方法。生成的3D场景在保留连续和离散特征模式的同时忠实地插入训练数据。

深度学习对多视图立体声系统产生了重大影响。最先进的方法通常涉及构建成本量，然后是多个3D卷积操作来恢复输入图像的像素方面深度。虽然这种平面扫描立体声的最终学习推进了公共基准的准确性，但它们通常很慢。我们展示了一个高效的多视图立体声算法，通过注意机制将多视图约束无缝地集成到单视网中。由于\ Ouralg仅在2D卷积上建立，它比所有值得注意的对应物更快2美元。此外，我们的算法产生精确的深度估计和3D重建，实现最先进的结果，以具有挑战性的基准剪刀，Sun3D，RGBD和古典DTU数据集。我们的算法还在Inexact相机姿势的设置中进行了所有其他算法。我们的代码在\ url {https:/github.com/zhenpeiyang/mvs2d}释放

This paper addresses the challenge of 6DoF pose estimation from a single RGB image under severe occlusion or truncation. Many recent works have shown that a two-stage approach, which first detects keypoints and then solves a Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable performance. However, most of these methods only localize a set of sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise Voting Network (PVNet) to regress pixel-wise unit vectors pointing to the keypoints and use these vectors to vote for keypoint locations using RANSAC. This creates a flexible representation for localizing occluded or truncated keypoints. Another important feature of this representation is that it provides uncertainties of keypoint locations that can be further leveraged by the PnP solver. Experiments show that the proposed approach outperforms the state of the art on the LINEMOD, Occlusion LINEMOD and YCB-Video datasets by a large margin, while being efficient for real-time pose estimation. We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation. The code will be avaliable at https://zju-3dv.github.io/pvnet/.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.