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2S-UDF: A Novel Two-Stage UDF Learning Method for Robust Non-Watertight Model Reconstruction from Multi-View Images

2S-UDF: A Novel Two-Stage UDF Learning Method for Robust Non...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Junkai Deng Fei Hou Xuhui Chen Wencheng Wang Ying He State Key Laboratory of Computer Science Chinese Academy of Sciences Institute of Software University of Chinese Academy of Sciences School of Computer Science and Engineering Nanyang Technological University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Recently, building on the foundation of neural radiance field, various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet, a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned distance values into volume density, ensuring that the resulting weight function remains unbiased and sensitive to occlusions. Falling short on these requirements often results in incorrect topology or large reconstruction errors in resulting models. This paper addresses this challenge by presenting a novel two-stage algorithm, 2S-UDF, for learning a high-quality UDF from multi-view images. Initially, the method applies an easily trainable density function that, while slightly biased and transparent, aids in coarse reconstruction. The subsequent stage then refines the geometry and appearance of the object to achieve a high-quality reconstruction by directly adjusting the weight function used in volume rendering to ensure that it is unbiased and occlusion-aware. Decoupling density and weight in two stages makes our training stable and robust, distinguishing our technique from existing UDF learning approaches. Evaluations on the DeepFashion3D, DTU, and BlendedMVS datasets validate the robustness and effectiveness of our proposed approach. In both quantitative metrics and visual quality, the results indicate our superior performance over other UDF learning techniques in reconstructing 3D non-watertight models from multi-view images. Our code is available at https://***/jkdeng/2sudf/.

关键词： Training Solid modeling Visualization Three-dimensional displays Accuracy Rendering (computer graphics) Density functional theory

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Aligning and Prompting Everything All at Once for Universal Visual Perception

Aligning and Prompting Everything All at Once for Universal ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yunhang Shen Chaoyou Fu Peixian Chen Mengdan Zhang Ke Li Xing Sun Yunsheng Wu Shaohui Lin Rongrong Ji Tencent Youtu Lab School of Computer Science and Technology East China Normal University Shanghai China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University P.R. China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

vision foundation models have been explored recently to build general-purpose vision systems. However, predomi-nant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality in-teraction, which is not effective in prompting object detection and visual grounding. Another line of work that fo-cuses on pixel-level tasks often encounters a large annotation gap of things and stuff, and suffers from mutual inter-ference between foreground-object and background-class segmentation. In stark contrast to the prevailing methods, we present APE, a universal visual perception model for aligning and prompting everything all at once in an image to perform diverse tasks, i.e., detection, segmentation, and grounding, as an instance-level sentence-object matching paradigm. Specifically, APE advances the convergence of detection and grounding by reformulating language-guided grounding as open-vocabulary detection, which efficiently scales up model prompting to thousands of category vocab-ularies and region descriptions while maintaining the ef-fectiveness of cross-modality fusion. To bridge the granu-larity gap of different pixel-level tasks, APE equalizes se-mantic and panoptic segmentation to proxy instance learning by considering any isolated regions as individual in-stances. APE aligns vision and language representation on broad data with natural and challenging characteristics all at once without task-specific fine-tuning. The extensive ex-periments on over 160 datasets demonstrate that, with only one-suit of weights, APE outperforms (or is on par with) the state-of-the-art models, proving that an effective yet univer-sal perception for anything aligning and prompting is in-deed feasible. Codes and trained models are released at https://***/shenyunhang/APE.

关键词： Image segmentation Vocabulary Visualization Grounding Annotations Machine vision Object detection

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Move as you Say, Interact as you can: Language-Guided Human Motion Generation with Scene Affordance

Move as you Say, Interact as you can: Language-Guided Human ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zan Wang Yixin Chen Baoxiong Jia Puhao Li Jinlu Zhang Jingze Zhang Tengyu Liu Yixin Zhu Wei Liang Siyuan Huang School of Computer Science & Technology Beijing Institute of Technology National Key Laboratory of General Artificial Intelligence BIGAI Dept. of Automation Tsinghua University CFCS School of Computer Science Peking University Institute for AI Peking University Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Despite significant advancements in text-to-motion syn-thesis, generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i) the absence of powerful generative models capable of jointly modeling natural language, 3D scenes, and human motion, and (ii) the generative models' in-tensive data requirements contrasted with the scarcity of comprehensive, high-quality, language-scene-motion datasets. To tackle these issues, we introduce a novel two-stage frame-work that employs scene affordance as an intermediate representation, effectively linking 3D scene grounding and conditional motion generation. Our framework comprises an Affordance Diffusion Model (ADM) for predicting ex-plicit affordance map and an Affordance-to-Motion Diffusion Model (AMDM) for generating plausible human motions. By leveraging scene affordance maps, our method overcomes the difficulty in generating human motion under multimodal condition signals, especially when training with limited data lacking extensive language-scene-motion pairs. Our exten-sive experiments demonstrate that our approach consistently outperforms all baselines on established benchmarks, in-cluding HumanML3D and HUMANISE. Additionally, we validate our model's exceptional generalization capabilities on a specially curated evaluation set featuring previously unseen descriptions and scenes.

关键词： Training Solid modeling Adaptation models Three-dimensional displays Grounding Affordances Diffusion models

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High-fidelity Person-centric Subject-to-Image Synthesis

High-fidelity Person-centric Subject-to-Image Synthesis

引用

conference on computer vision and pattern recognition (CVPR)

作者： Yibin Wang Weizhong Zhang Jianwei Zheng Cheng Jin School of Computer Science Fudan University College of Computer Science and Technology Zhejiang University of Technology School of Data Science Fudan University Haina Lab

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Current subject-driven image generation methods en-counter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion, which involves an irreconcilable training imbalance. Precisely, to generate realistic persons, they need to sufficiently tune the pre-trained model, which inevitably causes the model to forget the rich semantic scene prior and makes scene generation over-fit to the training data. Moreover, even with sufficient fine-tuning, these methods can still not generate high-fidelity persons since joint learning of the scene and person generation also lead to quality compromise. In this paper, we propose Face-diffuser, an effective collaborative generation pipeline to eliminate the above training imbal-ance and quality compromise. Specifically, we first develop two specialized pre-trained diffusion models, i.e., Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM), for scene and person generation, respectively. The sampling process is divided into three sequential stages, i.e., semantic scene construction, subject-scene fusion, and subject enhancement. The first and last stages are performed by TDM and SDM respectively. The subject-scene fusion stage, that is the collaboration achieved through a novel and highly effective mechanism, Saliency-adaptive Noise Fusion (SNF). Specifically, it is based on our key observation that there exists a robust link between classifier-free guidance responses and the saliency of generated images. In each time step, SNF leverages the unique strengths of each model and allows for the spatial blending of predicted noises from both models automatically in a saliency-aware manner, all of which can be seamlessly integrated into the DDIM sampling process. Extensive experiments confirm the impressive effectiveness and robustness of the Face-diffuser in gener-ating high-fidelity person images depi

关键词： Training Image synthesis Semantics Noise Pipelines Collaboration Predictive models

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Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching

Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point...

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conference on computer vision and pattern recognition (CVPR)

作者： Matteo Bastico Etienne Decencière Laurent Corté Yannick Tillier David Ryckelynck Mines Paris Université PSL Centre des Matériaux (MAT) UMR7633 CNRS Evry France Mines Paris Université PSL Centre de Morphologie Mathématique (CMM) Fontainebleau France Mines Paris Université PSL Centre de Mise en Forme des Matériaux (CEMEF) UMR7635 CNRS Sophia Antipolis France

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing lo-cal differences is crucial for accurately identifying a cor-rect match, thereby enhancing the overall robustness and reliability of the matching process. Commonly used shape descriptors have several limitations and often fail to provide meaningful local insights about the paired geome-tries. In this work, we propose a new technique, based on graph Laplacian eigenmaps, to match point clouds by taking into account fine local structures. To deal with the order and sign ambiguity of Laplacian eigenmaps, we in-troduce a new operator, called Coupled Laplacian 1 1 Code: https://***/matteo-bastico/CoupLap, that allows to easily generate aligned eigenspaces for multiple registered geometries. We show that the similarity between those aligned high-dimensional spaces provides a locally meaningful score to match shapes. We firstly evaluate the performance of the proposed technique in a point-wise man-ner, focusing on the task of object anomaly localization on the MVTec 3D-AD dataset. Additionally, we define a new medical task, called automatic Bone Side Estimation (BSE), which we address through a global similarity score derived from coupled eigenspaces. In order to test it, we propose a benchmark collecting bone surface structures from various public datasets. Our matching technique, based on Cou-pled Laplacian, outperforms other methods by reaching an impressive accuracy on both tasks.

关键词： Point cloud compression Location awareness Geometry computer vision Laplace equations Three-dimensional displays Shape

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NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods and Results

NTIRE 2022 Challenge on High Dynamic Range Imaging: Methods ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Eduardo Pé rez-Pellitero Sibi Catley-Chandar Richard Shaw Aleš Leonardis Radu Timofte Zexin Zhang Cen Liu Yunbo Peng Yue Lin Gaocheng Yu Jin Zhang Zhe Ma Hongbin Wang Xiangyu Chen Xintao Wang Haiwei Wu Lin Liu Chao Dong Jiantao Zhou Qingsen Yan Song Zhang Weiye Chen Yuhang Liu Zhen Zhang Yanning Zhang Javen Qinfeng Shi Dong Gong Dan Zhu Mengdi Sun Guannan Chen Yang Hu Haowei Li Baozhu Zou Zhen Liu Wenjie Lin Ting Jiang Chengzhi Jiang Xinpeng Li Mingyan Han Haoqiang Fan Jian Sun Shuaicheng Liu Juan Marí n-Vega Michael Sloth Peter Schneider-Kamp Richard Rö ttger Chunyang Li Long Bao Gang He Ziyao Xu Li Xu Gen Zhan Ming Sun Xing Wen Junlin Li Jinjing Li Chenghua Li Ruipeng Gang Fangya Li Chenming Liu Shuang Feng Fei Lei Rui Liu Junxiang Ruan Tianhong Dai Wei Li Zhan Lu Hengyan Liu Peian Huang Guangyu Ren Yonglin Luo Chang Liu Qiang Tu Sai Ma Yizhen Cao Steven Tel Barthelemy Heyrman Dominique Ginhac Chul Lee Gahyeon Kim Seonghyun Park An Gia Vien Truong Thanh Nhat Mai Howoon Yoon Tu Vo Alexander Holston Sheir Zaheer Chan Y. Park Huawei Noah&#x2019 s Ark Laboratory ETH Z&#x00FC rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the challenge on constrained high dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2022. This manuscript focuses on the competition set-up, datasets, the proposed methods and their results. The challenge aims at estimating an HDR image from multiple respective low dynamic range (LDR) observations, which might suffer from under-or over-exposed regions and different sources of noise. The challenge is composed of two tracks with an emphasis on fidelity and complexity constraints: In Track 1, participants are asked to optimize objective fidelity scores while imposing a low-complexity constraint (i.e. solutions can not exceed a given number of operations). In Track 2, participants are asked to minimize the complexity of their solutions while imposing a constraint on fidelity scores (i.e. solutions are required to obtain a higher fidelity score than the prescribed baseline). Both tracks use the same data and metrics: Fidelity is measured by means of PSNR with respect to a ground-truth HDR image (computed both directly and with a canonical tonemapping operation), while complexity metrics include the number of Multiply-Accumulate (MAC) operations and runtime (in seconds).

关键词： computer vision Runtime conferences Imaging Dynamic range Particle measurements Market research

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Text-to-3D using Gaussian Splatting

Text-to-3D using Gaussian Splatting

引用

conference on computer vision and pattern recognition (CVPR)

作者： Zilong Chen Feng Wang Yikai Wang Huaping Liu Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the optimization of volume rendering has achieved remarkable progress in synthesizing realistic 3D objects. Yet most existing text-to-3D methods by SDS and volume rendering suffer from inaccurate geometry, e.g., the Janus issue, since it is hard to explicitly integrate 3D priors into implicit 3D representations. Besides, it is usually time-consuming for them to generate elaborate 3D models with rich colors. In response, this paper proposes GSGEN, a novel method that adopts Gaussian Splatting, a recent state-of-the-art representation, to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under 3D point cloud diffusion prior along with the ordinary 2D SDS optimization, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative appearance refinement to enrich texture details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D assets with delicate details and accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. Our code is available at https://***/gsgen3d/gsgen.

关键词： Geometry Point cloud compression Solid modeling Three-dimensional displays Shape Image color analysis Rendering (computer graphics)

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RELI11D: A Comprehensive Multimodal Human Motion Dataset and Method

RELI11D: A Comprehensive Multimodal Human Motion Dataset and...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Ming Yan Yan Zhang Shuqiang Cai Shuqi Fan Xincheng Lin Yudi Dai Siqi Shen Chenglu Wen Lan Xu Yuexin Ma Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Comprehensive capturing of human motions requires both accurate captures of complex poses and precise localization of the human within scenes. Most of the HPE datasets and methods primarily rely on RGB, LiDAR, or IMU data. However, solely using these modalities or a combination of them may not be adequate for HPE, particularly for complex and fast movements. For holistic human motion understanding, we present RELI11D, a high-quality multimodal human motion dataset involves LiDAR, IMU system, RGB camera, and Event camera. It records the motions of 10 actors performing 5 sports in 7 scenes, including 3.32 hours of synchronized LiDAR point clouds, IMU measurement data, RGB videos and Event steams. Through extensive experiments, we demonstrate that the RELI 11 D presents considerable challenges and opportunities as it contains many rapid and complex motions that require precise location. To address the challenge of integrating different modalities, we propose LEIR, a multimodal baseline that effectively utilizes LiDAR Point Cloud, Event stream, and RGB through our cross-attention fusion strategy. We show that LEIR exhibits promising results for rapid motions and daily motions and that utilizing the characteristics of multiple modalities can indeed improve HPE performance. Both the dataset and source code release publicly in http://***/reli11d/, fostering collaboration and enabling further exploration in this field.

关键词： Point cloud compression Location awareness Laser radar Source coding Cameras Motion detection pattern recognition

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HIT: Estimating Internal Human Implicit Tissues from the Body Surface

HIT: Estimating Internal Human Implicit Tissues from the Bod...

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conference on computer vision and pattern recognition (CVPR)

作者： Marilyn Keller Vaibhav Arora Abdelmouttaleb Dakri Shivam Chandhok Jürgen Machann Andreas Fritsche Michael J. Black Sergi Pujades Max Planck Institute for Intelligent Systems Tübingen Germany Université Grenoble Alpes Inria CNRS Grenoble INP LJK France IDM Helmholtz Center Munich at the University of Tübingen

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

The creation of personalized anatomical digital twins is important in the fields of medicine, computer graphics, sports science, and biomechanics. To observe a subject's anatomy, expensive medical devices (MRI or CT) are required and the creation of the digital model is often time-consuming and involves manual effort. Instead, we leverage the fact that the shape of the body surface is correlated with the internal anatomy; e.g. from surface observations alone, one can predict body composition and skeletal structure. In this work, we go further and learn to infer the 3D location of three important anatomic tissues: subcutaneous adipose tissue (fat), lean tissue (muscles and organs), and long bones. To learn to infer these tissues, we tackle several key challenges. We first create a dataset of human tissues by segmenting full-body MRI scans and registering the SMPL body mesh to the body surface. With this dataset, we train HIT (Human Implicit Tissues), an implicit function that, given a point inside a body, predicts its tissue class. HIT leverages the SMPL body model shape and pose parameters to canonicalize the medical data. Unlike SMPL, which is trained from upright 3D scans, MRI scans are acquired with subjects lying on a table, resulting in significant soft-tissue deformation. Consequently, HIT uses a learned volumetric deformation field that undoes these deformations. Since HIT is parameterized by SMPL, we can repose bodies or change the shape of subjects and the internal structures deform appropriately. We perform extensive experiments to validate HIT's ability to predict a plausible internal structure for novel subjects. The dataset and HIT model are available at https://*** to foster future research in this direction.

关键词： Three-dimensional displays Shape Deformation Magnetic resonance imaging Computational modeling Muscles pattern recognition

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Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection

Real-IAD: A Real-World Multi-View Dataset for Benchmarking V...

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conference on computer vision and pattern recognition (CVPR)

作者： Chengjie Wang Wenbing Zhu Bin-Bin Gao Zhenye Gan Jiangning Zhang Zhihao Gu Shuguang Qian Mingang Chen Lizhuang Ma Shanghai Jiao Tong University Youtu Lab Tencent Fudan University Ronacheer Co. Ltd Shanghai Development Center of Computer Software Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Industrial anomaly detection (I AD) has garnered signif-icant attention and experienced rapid development. However, the recent development of I AD approach has encountered certain difficulties due to dataset limitations. On the one hand, most of the state-of-the-art methods have achieved saturation (over 99% in AUROC) on mainstream datasets such as MVTec, and the differences of methods cannot be well distinguished, leading to a significant gap between public datasets and actual application scenarios. On the other hand, the research on various new practical anomaly detection settings is limited by the scale of the dataset, posing a risk of overfitting in evaluation results. Therefore, we propose a large-scale, Real-world, and multi-view Industrial Anomaly Detection dataset, named Real- I AD, which contains 150K high-resolution images of 30 different objects, an order of magnitude larger than existing datasets. It has a larger range of defect area and ratio proportions, making it more challenging than previous datasets. To make the dataset closer to real application scenarios, we adopted a multi-view shooting method and proposed sample-level evaluation metrics. In addition, beyond the general unsupervised anomaly detection setting, we propose a new setting for Fully Unsupervised Indus-trial Anomaly Detection (FUIAD) based on the observation that the yield rate in industrial production is usually greater than 60%, which has more practical application value. Finally, we report the results of popular I AD methods on the Real- I AD dataset, providing a highly challenging benchmark to promote the development of the I AD field.

关键词： Training Measurement Noise Metals Production Benchmark testing Inspection

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