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检索条件"主题词=3D Computer Vision"

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SphereCraft: A dataset for Spherical Keypoint detection, Matching and Camera Pose Estimation

SphereCraft: A Dataset for Spherical Keypoint Detection, Mat...

引用

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

作者： Gava, Christiano Cho, Yunmin Raue, Federico Palacio, Sebastian Pagani, Alain dengel, Andreas DFKI Kaiserslautern Germany Aimmo Germany GmbH Dusseldorf Germany Univ Kaiserslautern Landau Kaiserslautern Germany

ISBN: (纸本)9798350318920;9798350318937

This paper introduces SphereCraft, a dataset specifically designed for spherical keypoint detection, matching, and camera pose estimation. The dataset addresses the limitations of existing datasets by providing extracted keypoints from various detectors, along with their ground truth correspondences. Synthetic scenes with photo-realistic rendering and accurate 3d meshes are included, as well as real-world scenes acquired from different spherical cameras. SphereCraft enables the development and evaluation of algorithms targeting multiple camera viewpoints, advancing the state-of-the-art in computer vision tasks involving spherical images. Our dataset is available at https://***/spherecraftweb/.

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

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FIRe: Fast Inverse Rendering using directional and Signed distance Functions

FIRe: Fast Inverse Rendering using Directional and Signed Di...

引用

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

作者： Yenamandra, Tarun Tewari, Ayush Yang, Nan Bernard, Florian Theobalt, Christian Cremers, daniel Tech Univ Munich MCML Munich Germany MIT 77 Massachusetts Ave Cambridge MA 02139 USA Univ Bonn Bonn Germany MPI Informat SIC Frankfurt Germany

ISBN: (纸本)9798350318920;9798350318937

Neural 3d implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3d reconstruction. A major downside of existing approaches while rendering an image is that they require evaluating the network multiple times per camera ray so that the high computational time forms a bottleneck for downstream applications. We address this problem by introducing a novel neural scene representation that we call the directional distance function (ddF). To this end, we learn a signed distance function (SdF) along with our ddF model to represent a class of shapes. Specifically, our ddF is defined on the unit sphere and predicts the distance to the surface along any given direction. Therefore, our ddF allows rendering images with just a single network evaluation per camera ray. Based on our ddF, we present a novel fast algorithm (FIRe) to reconstruct 3d shapes given a posed depth map. We evaluate our proposed method on 3d reconstruction from single-view depth images, where we empirically show that our algorithm reconstructs 3d shapes more accurately and it is more than 15 times faster (per iteration) than competing methods.

关键词： 3d computer vision Algorithms

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CrossMoCo: Multi-modal Momentum Contrastive Learning for Point Cloud 20

CrossMoCo: Multi-modal Momentum Contrastive Learning for Poi...

引用

20th Conference on Robots and vision (CRV)

作者： Paul, Sneha Patterson, Zachary Bouguila, Nizar Concordia Univ Concordia Inst Informat Syst Engn CIISE Montreal PQ Canada

ISBN: (纸本)9798350341393

The point cloud is a 3d geometric data that lacks a specific structure and is permutation-invariant. The applications of point clouds have gained significant attention recently in the field of vision tasks. However, most existing works on point clouds utilize supervised learning on large labelled data, which are costly and laborious to collect. To this end, unsupervised learning, for example, self-supervised learning, has shown promising performance in various tasks of 2d computer vision and holds the potential in 3d computer vision applications. In this study, we introduce a novel self-supervised method called CrossMoCo, which learns the representations of unlabelled point cloud data in a multi-modal setup that also utilizes the 2d rendered images of the point clouds. CrossMoCo outperforms existing methods on multi-modal self-supervised learning on point cloud by introducing two new concepts: momentum contrastive learning with more negative samples and multiple-view intra-modal contrastive learning. The first component learns from an online encoder and a momentum encoder with a large number of negative samples, which provides consistent learning signals. The second component enforces consistency between different views of the samples of the same modality, thereby improving multi-modal representation. We conduct extensive studies on two popular benchmark datasets (ModelNet40 and ScanObjectNN) for linear classification and few-shot learning tasks. Our results demonstrate that CrossMoCo achieves superior performance over existing methods for both tasks on both datasets, achieving up to 4.36% improvement on linear classification and up to 9.2% on few-shot tasks. Our code is available at https://***/snehaputul/CrossMoCo.

关键词： 3d computer vision self-supervised learning contrastive learning few-shot learning

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BALF: Simple and Efficient Blur Aware Local Feature detector

BALF: Simple and Efficient Blur Aware Local Feature Detector

引用

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

作者： Zhao, Zhenjun Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under low-lighting conditions. To address this issue, we propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image. The network is also light-weight and able to run in real-time, which enables its deployment for time-constrained applications. Extensive experimental results demonstrate that our detector is able to improve the detection repeatability with blurred images, while keeping comparable performance as existing state-of-the-art detectors for sharp images. The code and trained weights are publicly available at ***/ericzzj1989/BALF.

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

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Scene Adaptive RGB-d Based Oscillation Sensing for a Multi Flexible Link Robot Arm in Unstructured dynamic Environments

Scene Adaptive RGB-D Based Oscillation Sensing for a Multi F...

引用

25th IEEE\RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Malzahn, J. Phung, A. S. Bertram, T. Tech Univ Dortmund Inst Control Theory & Syst Engn D-44221 Dortmund Germany

ISBN: (纸本)9781467317368

The paper experimentally compares six visual oscillation sensing approaches for a three degrees of freedom flexible link robot arm with an eye-in-hand RGB-d camera. The comparison includes five representative scenarios. Based upon the results the authors propose a novel scene adaptive camera motion reconstruction scheme. The scheme adaptively selects the best approach according to the actual scene texture and depth profile. Experiments in indoor scenarios with sparse texture, poor depth profiles as well as dynamic scene contents approve the obtained signal quality to be well suited for visual vibration damping of flexible link robot arms in a great variety of frequently observed scenarios.

关键词： Multi link flexible robots visual oscillation sensing 3d computer vision

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detection By Registration

Detection By Registration

引用

Intelligent Systems Conference (IntelliSys)

作者： Greenspan, Michael Xu, Liwen Chau, Jason Queens Univ Dept Elect & Comp Engn Kingston ON Canada Epson Edge Markham ON Canada

ISBN: (纸本)9781509064359

The detection by Registration (dbR) method is proposed as an efficient and accurate solution to the bin picking problem. dbR exploits the relatively large convergence basin of the Iterative Closest Point (ICP) algorithm, by specifying a set of characteristic poses from which to initiate ICP for a given part. For each characteristic pose, an efficient search is executed at a set of uniform seed point translations in a scene, by incrementing the pose through a small set of discrete in-plane rotations. The results from this search are then refined further using ICP and validated. Experiments have shown favorable performance, with an average of 600% detection accuracy and an average execution time of less than 400 msec per scene.

关键词： 3d computer vision registration bin picking

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Generation of Upright Panoramic Image from Non-upright Panoramic Image

Generation of Upright Panoramic Image from Non-upright Panor...

引用

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

作者： Liu, Jingguo Chen, Heyu Li, Shigang Li, Jianfeng Southwest Univ Coll Elect & Informat Engn Chongqing Peoples R China Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan

ISBN: (纸本)9798350318920;9798350318937

The inclination of a spherical camera results in nonupright panoramic images. To carry out upright adjustment, traditional methods estimate camera inclination angles firstly, and then resample the image in terms of the estimated rotation to generate upright image. Since sampling an image is a time-consuming processing, a lookup table is usually used to achieve a high processing speed;however, the content of a lookup table depends on the rotational angles and needs extra memory to store also. In this paper we propose a new approach for panorama upright adjustment, which directly generates an upright panoramic image from an input nonupright one without rotation estimation and lookup tables as an intermediate processing. The proposed approach formulates panorama upright adjustment as a pixelwise image-to-image mapping problem, and the mapping is directly generated from an input nonupright panoramic image via an end-to-end neural network. As shown in the experiment of this paper, the proposed method results in a lightweight network, as less as 163MB, with high processing speed, as great as 9ms, for a 256x512 pixel panoramic image.

关键词： 3d 3d computer vision Algorithms Algorithms Applications etc. Generative models for image video Virtual / augmented reality

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MonoProb: Self-Supervised Monocular depth Estimation with Interpretable Uncertainty

MonoProb: Self-Supervised Monocular Depth Estimation with In...

引用

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

作者： Marsal, Remi Chabot, Florian Loesch, Angelique Grolleau, William Sahbi, Hichem Univ Paris Saclay LIST CEA F-91120 Palaiseau France Sorbonne Univ CNRS LIP6 c F-75005 Paris France

ISBN: (纸本)9798350318920;9798350318937

Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfections of these approaches, a quantification of the prediction confidence is crucial to guide decision-making systems that rely on depth estimation. In this paper, we propose MonoProb, a new unsupervised monocular depth estimation method that returns an interpretable uncertainty, which means that the uncertainty reflects the expected error of the network in its depth predictions. We rethink the stereo or the structure-from-motion paradigms used to train unsupervised monocular depth models as a probabilistic problem. Within a single forward pass inference, this model provides a depth prediction and a measure of its confidence, without increasing the inference time. We then improve the performance on depth and uncertainty with a novel self-distillation loss for which a student is supervised by a pseudo ground truth that is a probability distribution on depth output by a teacher. To quantify the performance of our models we design new metrics that, unlike traditional ones, measure the absolute performance of uncertainty predictions. Our experiments highlight enhancements achieved by our method on standard depth and uncertainty metrics as well as on our tailored metrics. https://***/CEA-LIST/MonoProb

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

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RIMeshGNN: A Rotation-Invariant Graph Neural Network for Mesh Classification

RIMeshGNN: A Rotation-Invariant Graph Neural Network for Mes...

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

作者： Shakibajahromi, Baharch Kim, Edward Breen, david E. Drexel Univ Dept Comp Sci Philadelphia PA 19104 USA

ISBN: (纸本)9798350318920;9798350318937

Shape analysis tasks, including mesh classification, segmentation, and retrieval demonstrate symmetries in Euclidean space and should be invariant to geometric transformations such as rotation and translation. However, existing methods in mesh analysis often rely on extensive data augmentation and more complex analysis models to handle 3d rotations. despite these efforts, rotation invariance is not guaranteed, which can significantly reduce accuracy when test samples undergo arbitrary rotations, because the analysis method struggles to generalize to the unknown orientations of the test samples. To address these challenges, our work presents a novel approach that employs graph neural networks (GNNs) to analyze mesh-structured data. Our proposed GNN layer, aggregation function, and local pooling layer are equivariant to the rotation, reflection and translation of 3d shapes, making them suitable building blocks for our proposed rotation-invariant network for the classification of mesh models. Therefore, our proposed approach does not need rotation augmentation, and we can maintain accuracy even when test samples undergo arbitrary rotations. Extensive experiments on various datasets demonstrate that our methods achieve state-of-the-art performance.

关键词： 3d computer vision Algorithms

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Hyb-NeRF: A Multiresolution Hybrid Encoding for Neural Radiance Fields

Hyb-NeRF: A Multiresolution Hybrid Encoding for Neural Radia...

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

作者： Wang, Yifan Gong, Yi Zeng, Yuan SUSTech Shenzhen Peoples R China

ISBN: (纸本)9798350318920;9798350318937

Recent advances in Neural radiance fields (NeRF) have enabled high-fidelity scene reconstruction for novel view synthesis. However, NeRF requires hundreds of network evaluations per pixel to approximate a volume rendering integral, making it slow to train. Caching NeRFs into explicit data structures can effectively enhance rendering speed but at the cost of higher memory usage. To address these issues, we present Hyb-NeRF, a novel neural radiance field with a multi-resolution hybrid encoding that achieves efficient neural modeling and fast rendering, which also allows for high-quality novel view synthesis. The key idea of Hyb-NeRF is to represent the scene using different encoding strategies from coarse-to-fine resolution levels. Hyb-NeRF exploits memory-efficiency learnable positional features at coarse resolutions and the fast optimization speed and local details of hash-based feature grids at fine resolutions. In addition, to further boost performance, we embed cone tracing-based features in our learnable positional encoding that eliminates encoding ambiguity and reduces aliasing artifacts. Extensive experiments on both synthetic and realworld datasets show that Hyb-NeRF achieves faster rendering speed with better rending quality and even a lower memory footprint in comparison to previous state-of-the-art methods.

关键词： 3d computer vision Algorithms

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