Texture reconstruction techniques generally suffer from the errors in keyframe poses. We present a non-iterative method for seamless texture reconstruction of a given 3D scene. Our method finds the best texture alignment in a single shot using a global optimisation framework. First, we automatically select the best keyframe to texture each face of the mesh. This leads to a decomposition of the mesh into small groups of connected faces associated to a same keyframe. We call such groups fragments. Then, we propose a geometry-aware matching technique between the 3D keypoints extracted around the fragment borders, where the matching zone is controlled by the margin size. These constraints lead to a least squares (LS) model for finding the optimal alignment. Finally, visual seams are further reduced by applying a fast colour correction. In contrast to pixel-wise methods, we find the optimal alignment by solving a sparse system of linear equations, which is very fast and non-iterative. Experimental results demonstrate low computational complexity and outperformance compared to other alignment methods.
German Aerospace Center (DLR)（德国航空航天中心）；Technical University of Munich (TUM)（慕尼黑工业大学）
Tracking objects in 3D space and predicting their 6DoF pose is an essential task in computer vision. State-of-the-art approaches often rely on object texture to tackle this problem. However, while they achieve impressive results, many objects do not contain sufficient texture, violating the main underlying assumption. In the following, we thus propose ICG, a novel probabilistic tracker that fuses region and depth information and only requires the object geometry. Our method deploys correspondence lines and points to iteratively refine the pose. We also implement robust occlusion handling to im…
AlignVTOFF: Texture-Spatial Feature Alignment for High-Fidelity Virtual Try-Off
Sino-European School of Technology, Shanghai University（上海大学 sino-欧洲技术学院）；National Demonstration Center for Experimental Engineering Training Education, Shanghai University（上海大学国家级实验工程培训教育示范中心）
On the dynamic evolution of CLIP texture-shape bias and its relationship to human alignment and model robustness
Image Processing Lab, Universitat de Valencia, Spain（瓦伦西亚大学图像处理实验室）；Centro de Biomateriales e Ingenieria Tisular, Universitat Politecnica de Valencia, Spain（瓦伦西亚理工大学生物材料与组织工程中心）；Computer Vision Center, Spain（西班牙计算机视觉中心）；Universitat Autònoma de Barcelona, Spain（巴塞罗那自治大学）
University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
While recovery of geometry from image and video data has received a lot of attention in computer vision, methods to capture the texture for a given geometry are less mature. Specifically, classical methods for texture generation often assume clean geometry and reasonably well-aligned image data. While very recent methods, e.g., adversarial texture optimization, better handle lower-quality data obtained from hand-held devices, we find them to still struggle frequently. To improve robustness, particularly of recent adversarial texture optimization, we develop an explicit initialization and an al…
Dyson Robotics Laboratory at Imperial College（帝国理工学院戴森机器人实验室）
We argue that robust dense SLAM systems can make valuable use of the layers of features coming from a standard CNN as a pyramid of `semantic texture' which is suitable for dense alignment while being much more robust to nuisance factors such as lighting than raw RGB values. We use a straightforward Lucas-Kanade formulation of image alignment, with a schedule of iterations over the coarse-to-fine levels of a pyramid, and simply replace the usual image pyramid by the hierarchy of convolutional feature maps from a pre-trained CNN. The resulting dense alignment performance is much more robust to l…
Northwestern Polytechnical University（西北工业大学）；Ningbo Institute, Northwestern Polytechnical University（西北工业大学宁波研究院）；Nanyang Technological University（南洋理工大学）；The University of Adelaide（阿德莱德大学）
In the current person Re-identification (ReID) methods, most domain generalization works focus on dealing with style differences between domains while largely ignoring unpredictable camera view change, which we identify as another major factor leading to a poor generalization of ReID methods. To tackle the viewpoint change, this work proposes to use a 3D dense pose estimation model and a texture mapping module to map the pedestrian images to canonical view images. Due to the imperfection of the texture mapping module, the canonical view images may lose the discriminative detail clues from the …
Korea Advanced Institute of Science and Technology（韩国科学技术院）
Editing hairstyle is unique and challenging due to the complexity and delicacy of hairstyle. Although recent approaches significantly improved the hair details, these models often produce undesirable outputs when a pose of a source image is considerably different from that of a target hair image, limiting their real-world applications. HairFIT, a pose-invariant hairstyle transfer model, alleviates this limitation yet still shows unsatisfactory quality in preserving delicate hair textures. To solve these limitations, we propose a high-performing pose-invariant hairstyle transfer model equipped …
Smart retail stores are becoming the fact of our lives. Several computer vision and sensor based systems are working together to achieve such a complex and automated operation. Besides, the retail sector already has several open and challenging problems which can be solved with the help of pattern recognition and computer vision methods. One important problem to be tackled is the planogram compliance control. In this study, we propose a novel method to solve it. The proposed method is based on object detection, planogram compliance control, and focused and iterative search steps. The object de…
This paper presents a fast projection mapping method for moving image content projected onto a markerless planar surface using a low-latency Digital Micromirror Device (DMD) projector. By adopting a closed-loop alignment approach, in which not only the surface texture but also the projected image is tracked by a camera, the proposed method is free from a calibration or position adjustment between the camera and projector. We designed fiducial patterns to be inserted into a fast flapping sequence of binary frames of the DMD projector, which allows the simultaneous tracking of the surface textur…
Deformable surface tracking from monocular images is well-known to be under-constrained. Occlusions often make the task even more challenging, and can result in failure if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well a…
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Microsoft（微软）；University of Maryland, College Park（马里兰大学帕克分校）
This research strives for natural language moment retrieval in long, untrimmed video streams. The problem is not trivial especially when a video contains multiple moments of interests and the language describes complex temporal dependencies, which often happens in real scenarios. We identify two crucial challenges: semantic misalignment and structural misalignment. However, existing approaches treat different moments separately and do not explicitly model complex moment-wise temporal relations. In this paper, we present Moment Alignment Network (MAN), a novel framework that unifies the candida…
Mi-Ripple: Restoring Images Degraded by Iterative AI Editing
Miyang Technology (Shanghai) Co., Ltd.（米扬科技（上海）有限公司）；Key Laboratory of System Software (Chinese Academy of Sciences)（中国科学院系统软件重点实验室）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Jiao Tong University（上海交通大学）；Tianjin University（天津大学）
Tsinghua University（清华大学）；BNRist（北京国家信息科学与技术研究中心）；University of Washington（华盛顿大学）
While category-level 9DoF object pose estimation has emerged recently, previous correspondence-based or direct regression methods are both limited in accuracy due to the huge intra-category variances in object shape and color, etc. Orthogonal to them, this work presents a category-level object pose and size refiner CATRE, which is able to iteratively enhance pose estimate from point clouds to produce accurate results. Given an initial pose estimate, CATRE predicts a relative transformation between the initial pose and ground truth by means of aligning the partially observed point cloud and an …
Huazhong University of Science and Technology（华中科技大学）；Huawei Cloud（华为云）
A key solution to temporal sentence grounding (TSG) exists in how to learn effective alignment between vision and language features extracted from an untrimmed video and a sentence description. Existing methods mainly leverage vanilla soft attention to perform the alignment in a single-step process. However, such single-step attention is insufficient in practice, since complicated relations between inter- and intra-modality are usually obtained through multi-step reasoning. In this paper, we propose an Iterative Alignment Network (IA-Net) for TSG task, which iteratively interacts inter- and in…
In this paper, we present feature-based technique for construction of mosaic image from underwater video sequence, which suffers from parallax distortion due to propagation properties of light in the underwater environment. The most of the available mosaic tools and underwater image mosaicing techniques yields final result with some artifacts such as blurring, ghosting and seam due to presence of parallax in the input images. The removal of parallax from input images may not reduce its effects instead it must be corrected in successive steps of mosaicing. Thus, our approach minimizes the paral…
Active Diffusion Matching: Score-based Iterative Alignment of Cross-Modal Retinal Images
ASRI, Dept. of ECE, Seoul National University（首尔大学电子与计算机工程系ASRI）；Dept. of Ophthalmology, Kangbuk Samsung Hospital, Sungkyunkwan University（成均馆大学三星首尔医院眼科）；School of Electrical Engineering, Kookmin University（国民大学电气工程学院）
NVIDIA（英伟达）；Simon Fraser University（西蒙弗雷泽大学）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）
In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large distortions, or use continuous texture fields that yield smooth outputs lacking details. We argue that the traditional way of representing textures with images and linking them to a 3D mesh via UV mapping is more desirable, since synthesizing 2D images is a well-studied problem. We propose AUV-Net which learns to embed 3D surfaces into a 2D aligned UV space, by mapp…
We provide a novel new approach for aligning geometric models using a dual graph structure where local features are mapping probabilities. Alignment of non-rigid structures is one of the most challenging computer vision tasks due to the high number of unknowns needed to model the correspondence. We have seen a leap forward using DNN models in template alignment and functional maps, but those methods fail for inter-class alignment where nonisometric deformations exist. Here we propose to rethink this task and use unrolling concepts on a dual graph structure - one for a forward map and one for a…
Texture-preserving implicit neural representation for Cone beam CT truncated reconstruction
National Key Research and Development Program of China（中华人民共和国国家重点研发计划）；National Natural Science Foundation of China（中华人民共和国国家自然科学基金）；Fundamental Research Funds for the Central Universities（中央高校基本科研业务费）
Understanding and extracting 3D information of objects from monocular 2D images is a fundamental problem in computer vision. In the task of 3D object pose estimation, recent data driven deep neural network based approaches suffer from scarcity of real images with 3D keypoint and pose annotations. Drawing inspiration from human cognition, where the annotators use a 3D CAD model as structural reference to acquire ground-truth viewpoints for real images; we propose an iterative Semantic Pose Alignment Network, called iSPA-Net. Our approach focuses on exploiting semantic 3D structural regularity t…
In this paper, we focus on person image generation, namely, generating person image under various conditions, e.g., corrupted texture or different pose. To address texture occlusion and large pose misalignment in this task, previous works just use the corresponding region's style to infer the occluded area and rely on point-wise alignment to reorganize the context texture information, lacking the ability to globally correlate the region-wise style codes and preserve the local structure of the source. To tackle these problems, we present a GLocal framework to improve the occlusion-aware texture…
Centre Hospitalier de Luxembourg（卢森堡中心医院）；Luxembourg Centre for Systems Biomedicine, University of Luxembourg（卢森堡大学卢森堡系统生物医学中心）；Trier University of Applied Sciences（特里尔应用科学大学）
The alignment of a set of objects by means of transformations plays an important role in computer vision. Whilst the case for only two objects can be solved globally, when multiple objects are considered usually iterative methods are used. In practice the iterative methods perform well if the relative transformations between any pair of objects are free of noise. However, if only noisy relative transformations are available (e.g. due to missing data or wrong correspondences) the iterative methods may fail. Based on the observation that the underlying noise-free transformations can be retrieved…
Zhejiang University（浙江大学）；Shanghai Innovation Institute（上海创新研究院）；Westlake University（西湖大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Fudan University（复旦大学）
Nanjing University of Information Science and Technology（南京信息工程大学）；University of Michigan（密歇根大学）
Robust high-dimensional data processing has witnessed an exciting development in recent years, as theoretical results have shown that it is possible using convex programming to optimize data fit to a low-rank component plus a sparse outlier component. This problem is also known as Robust PCA, and it has found application in many areas of computer vision. In image and video processing and face recognition, the opportunity to process massive image databases is emerging as people upload photo and video data online in unprecedented volumes. However, data quality and consistency is not controlled i…
