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检索条件"主题词=3D Computer Vision"

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Learning Robust Features and Latent Representations for Single View 3d Pose Estimation of Humans and Objects

Learning Robust Features and Latent Representations for Sing...

引用

作者： Bugra TEKIN ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE

学位级别：博士

Estimating the 3d poses of rigid and articulated bodies is one of the fundamental problems of computer vision. It has a broad range of applications including augmented reality, surveillance, animation and human-computer interaction. despite the ever-growing demand driven by the applications, predicting 3d pose from a 2d image is a challenging and ill-posed problem due to the loss of depth information during projection from 3d to 2d. Although there have been years of research on 3d pose estimation problem, it still remains unsolved. In this thesis, we propose a variety of ways to tackle the 3d pose estimation problem both for articulated human bodies and rigid object bodies by learning robust features and latent representations. First, we present a novel video-based approach that exploits spatiotemporal features for 3d human pose estimation in a discriminative regression scheme. While early approaches typically account for the motion information by temporally regularizing noisy pose estimates in individual frames, we demonstrate that taking into account motion information very early in the modeling process with spatiotemporal features yields significant performance improvements. We further propose a CNN-based motion compensation approach that stabilizes and centralizes the human body in the bounding boxes of consecutive frames to increase the reliability of spatiotemporal features. This then allows us to effectively overcome ambiguities and improve pose estimation accuracy. Second, we develop a novel deep Learning framework for structured prediction of 3d human pose. Our approach relies on an auto-encoder to learn a high-dimensional latent pose representa- tion that accounts for joint dependencies. We combine traditional CNNs for supervised learning with auto-encoders for structured learning and demonstrate that our approach outperforms the existing ones both in terms of structure preservation and prediction accuracy. Third, we propose a 3d human pose estimation appr

关键词： 3d human pose estimation 3d object pose estimation 6d pose estimation 3d computer vision motion compensation deep learning structured prediction

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dVMNet: Computing Relative Pose for Unseen Objects Beyond Hypotheses

DVMNet: Computing Relative Pose for Unseen Objects Beyond Hy...

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IEEE/CVF Conference on computer vision and Pattern Recognition (CVPR)

作者： Zhao, Chen Zhang, Tong dang, Zheng Salzmann, Mathieu Ecole Polytech Fed Lausanne Lausanne Switzerland ClearSpace SA Renens Switzerland

ISBN: (纸本)9798350353006

determining the relative pose of an object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically approximate the continuous pose representation with a large number of discrete pose hypotheses, which incurs a computationally expensive process of scoring each hypothesis at test time. By contrast, we present a deep Voxel Matching Network (dVMNet) that eliminates the need for pose hypotheses and computes the relative object pose in a single pass. To this end, we map the two input RGB images, reference and query, to their respective voxelized 3d representations. We then pass the resulting voxels through a pose estimation module, where the voxels are aligned and the pose is computed in an end-to-end fashion by solving a least-squares problem. To enhance robustness, we introduce a weighted closest voxel algorithm capable of mitigating the impact of noisy voxels. We conduct extensive experiments on the CO3d, LINEMOd, and Objaverse datasets, demonstrating that our method delivers more accurate relative pose estimates for novel objects at a lower computational cost compared to state-of-the-art methods. Our code is released at: https://***/sailor-z/dVMNet/.

关键词： 3d computer vision Multi-View Geometry Object Pose Estimation

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When 3d Bounding-Box Meets SAM: Point Cloud Instance Segmentation with Weak-and-Noisy Supervision

When 3D Bounding-Box Meets SAM: Point Cloud Instance Segment...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Yu, Qingtao due, Heming Liu, Chen Yu, Xin Univ Queensland Sch Informat & Elect Engn Brisbane Qld Australia Australian Natl Univ Coll Engn Computting & Cybernet Canberra ACT Australia

ISBN: (纸本)9798350318920;9798350318937

Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3d point cloud instance segmentation. However, we observed that existing methods would suffer severe performance degradation with perturbed bounding box annotations. To tackle this issue, we propose a complementary image prompt-induced weakly-supervised point cloud instance segmentation (CIP-WPIS) method. CIP-WPIS leverages pretrained knowledge embedded in the 2d foundation model SAM and 3d geometric prior to achieve accurate point-wise instance labels from the bounding box annotations. Specifically, CIP-WPIS first selects image views in which 3d candidate points of an instance are fully visible. Then, we generate complementary background and foreground prompts from projections to obtain SAM 2d instance mask predictions. According to these, we assign the confidence values to points indicating the likelihood of points belonging to the instance. Furthermore, we utilize 3d geometric homogeneity provided by superpoints to decide the final instance label assignments. In this fashion, we achieve high-quality 3d point-wise instance labels. Extensive experiments on both Scannet-v2 and S3dIS benchmarks proves that our method not only achieves state-of-the-art performance for bounding-boxes supervised point cloud instance segmentation, but also exhibits robustness against noisy 3d bounding-box annotations.

关键词： 3d computer vision Algorithms

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Learning to detect Ground Control Points for Improving the Accuracy of Stereo Matching 27

Learning to Detect Ground Control Points for Improving the A...

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27th IEEE Conference on computer vision and Pattern Recognition (CVPR)

作者： Spyropoulos, Aristotle Komodakis, Nikos Mordohai, Philippos Stevens Inst Technol Hoboken NJ 07030 USA Ecole Ponts ParisTech Paris France

ISBN: (纸本)9781479951178

While machine learning has been instrumental to the ongoing progress in most areas of computer vision, it has not been applied to the problem of stereo matching with similar frequency or success. We present a supervised learning approach for predicting the correctness of stereo matches based on a random forest and a set of features that capture various forms of information about each pixel. We show highly competitive results in predicting the correctness of matches and in confidence estimation, which allows us to rank pixels according to the reliability of their assigned disparities. Moreover, we show how these confidence values can be used to improve the accuracy of disparity maps by integrating them with an MRF-based stereo algorithm. This is an important distinction from current literature that has mainly focused on sparsification by removing potentially erroneous disparities to generate quasi-dense disparity maps.

关键词： 3d computer vision Stereo correspondence

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AssemblyNet: A Point Cloud dataset and Benchmark for Predicting Part directions in an Exploded Layout

AssemblyNet: A Point Cloud Dataset and Benchmark for Predict...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Gaarsdal, Jesper Haurum, Joakim Bruslund Wolff, Sune Madsen, Claus Brondgaard SynergyXR ApS Aarhus Denmark Aalborg Univ Visual Anal & Percept VAP Lab Aalborg Denmark Pioneer Ctr AI Aalborg Denmark Aalborg Univ Comp Graph Grp Aalborg Denmark

ISBN: (纸本)9798350318920;9798350318937

Exploded views are powerful tools for visualizing the assembly and disassembly of complex objects, widely used in technical illustrations, assembly instructions, and product presentations. Previous methods for automating the creation of exploded views are either slow and computationally costly or compromise on accuracy. Therefore, the construction of exploded views is typically a manual process. In this paper, we propose a novel approach for automatically predicting the direction of parts in an exploded view using deep learning. To achieve this, we introduce a new dataset, AssemblyNet, which contains point cloud data sampled from 3d models of real-world assemblies, including water pumps, mixed industrial assemblies, and LEGO models. The AssemblyNet dataset includes a total of 44 assemblies, separated into 495 subassemblies with a total of 5420 parts. We provide ground truth labels for regression and classification, representing the directions in which the parts are moved in the exploded views. We also provide performance benchmarks using various state-of-the-art models for shape classification on point clouds and propose a novel two-path network architecture. Project page available at https://***/jgaarsdal/AssemblyNet

关键词： 3d computer vision Algorithms Algorithms Applications datasets and evaluations Visualization

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HAMMER: Learning Entropy Maps to Create Accurate 3d Models in Multi-View Stereo

HAMMER: Learning Entropy Maps to Create Accurate 3D Models i...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Weilharter, Rafael Fraundorfer, Friedrich Graz Univ Technol Inst Comp Graph & Vis Graz Austria

ISBN: (纸本)9798350318920;9798350318937

While the majority of recent Multi-View Stereo Networks estimates a depth map per reference image, their performance is then only evaluated on the fused 3d model obtained from all images. This approach makes a lot of sense since ultimately the point cloud is the result we are mostly interested in. On the flip side, it often leads to a burdensome manual search for the right fusion parameters in order to score well on the public benchmarks. In this work, we tackle the aforementioned problem with HAMMER, a Hierarchical And Memory-efficient MVSNet with Entropy-filtered Reconstructions. We propose to learn a filtering mask based on entropy, which, in combination with a simple two-view geometric verification, is sufficient to generate high quality 3d models of any input scene. distinct from existing works, a tedious manual parameter search for the fusion step is not required. Furthermore, we take several precautions to keep the memory requirements for our method very low in the training as well as in the inference phase. Our method only requires 6 GB of GPU memory during training, while 3.6 GB are enough to process 1920x1024 images during inference. Experiments show that HAMMER ranks amongst the top published methods on the dTU and Tanks and Temples benchmarks in the official metrics, especially when keeping the fusion parameters fixed.

关键词： 3d computer vision Algorithms Algorithms and algorithms formulations Machine learning architectures

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Enhancing Multi-view Pedestrian detection Through Generalized 3d Feature Pulling

Enhancing Multi-view Pedestrian Detection Through Generalize...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Aung, Sithu Park, Haesol Jung, Hyungjoo Cho, Junghyun KIST Seongnam South Korea UST Daejeon South Korea Yonsei KIST Seoul South Korea

ISBN: (纸本)9798350318920;9798350318937

The main challenge in multi-view pedestrian detection is integrating view-specific features into a unified space for comprehensive end-to-end perception. Prior multi-view detection methods have focused on projecting perspective-view features onto the ground plane, creating a "bird's eye view" (BEV) representation of the scene. This paper proposes a simple but effective architecture that utilizes a non-parametric 3d feature-pulling strategy. This strategy directly extracts the corresponding 2d features for each valid voxel within the 3d feature volume, addressing the feature loss that may arise in previous methods. The proposed framework introduces three novel modules, each crafted to bolster the generalization capabilities of multi-view detection systems. Through extensive experiments, the efficacy of the proposed model is demonstrated. The results show a new state-of-the-art accuracy, both in conventional scenarios and particularly in the context of scene generalization benchmarks.

关键词： 3d computer vision Algorithms Algorithms Image recognition and understanding

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Joint 3d Shape and Motion Estimation from Rolling Shutter Light-Field Images

Joint 3D Shape and Motion Estimation from Rolling Shutter Li...

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： McGriff, Hermes Martins, Renato Andreff, Nicolas demonceaux, Cedric Univ Bourgogne CNRS UMR ICB 6303 Dijon France Univ Lorraine LORIA INRIA CNRS Lorraine France Univ Franche Comte CNRS UMR FEMTO ST 6174 Besancon France

ISBN: (纸本)9798350318920;9798350318937

In this paper, we propose an approach to address the problem of 3d reconstruction of scenes from a single image captured by a light-field camera equipped with a rolling shutter sensor. Our method leverages the 3d information cues present in the light-field and the motion information provided by the rolling shutter effect. We present a generic model for the imaging process of this sensor and a two-stage algorithm that minimizes the re-projection error while considering the position and motion of the camera in a motion-shape bundle adjustment estimation strategy. Thereby, we provide an instantaneous 3d shape-and-pose-and-velocity sensing paradigm. To the best of our knowledge, this is the first study to leverage this type of sensor for this purpose. We also present a new benchmark dataset composed of different light-fields showing rolling shutter effects, which can be used as a common base to improve the evaluation and tracking the progress in the field. We demonstrate the effectiveness and advantages of our approach through several experiments conducted for different scenes and types of motions. The source code and dataset are publicly available at: https://github. com/ ICBvision-AI/RSLF.

关键词： 3d computer vision Algorithms Algorithms Algorithms datasets and evaluations Low-level and physics-based vision

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A generic and flexible regularization framework for NeRFs

A generic and flexible regularization framework for NeRFs

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IEEE/CVF Winter Conference on Applications of computer vision (WACV)

作者： Ehret, Thibaud Mari, Roger Facciolo, Gabriele Univ Paris Saclay Ctr Borelli ENS Paris Saclay CNRS F-91190 Gif Sur Yvette France

ISBN: (纸本)9798350318920;9798350318937

Neural radiance fields, or NeRF, represent a breakthrough in the field of novel view synthesis and 3d modeling of complex scenes from multi-view image collections. Numerous recent works have shown the importance of making NeRF models more robust, by means of regularization, in order to train with possibly inconsistent and/or very sparse data. In this work, we explore how differential geometry can provide elegant regularization tools for robustly training NeRF-like models, which are modified so as to represent continuous and infinitely differentiable functions. In particular, we present a generic framework for regularizing different types of NeRFs observations to improve the performance in challenging conditions. We also show how the same formalism can also be used to natively encourage the regularity of surfaces by means of Gaussian or mean curvatures.

关键词： 3d computer vision Algorithms

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Optimizing Geometry Compression using Quantum Annealing

Optimizing Geometry Compression using Quantum Annealing

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IEEE Global Telecommunications Conference (GC Wkshps)

作者： Feld, Sebastian Friedrich, Markus Linnhoff-Popien, Claudia Ludwig Maximilians Univ Munchen Inst Comp Sci Munich Germany

ISBN: (纸本)9781538649206

The compression of geometry data is an important aspect of bandwidth-efficient data transfer for distributed 3d computer vision applications. We propose a quantum-enabled lossy 3d point cloud compression pipeline based on the constructive solid geometry (CSG) model representation. Key parts of the pipeline are mapped to NP-complete problems for which an efficient Ising formulation suitable for the execution on a Quantum Annealer exists. We describe existing Ising formulations for the maximum clique search problem and the smallest exact cover problem, both of which are important building blocks of the proposed compression pipeline. Additionally, we discuss the properties of the overall pipeline regarding result optimality and described Ising formulations.

关键词： optimized quantum applications 3d computer vision geometry processing constructive solid geometry

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