检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

249 篇 会议

馆藏范围

249 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

249 篇 工学
- 249 篇 计算机科学与技术...
- 83 篇 软件工程

主题

249 篇 3d from multi-vi...
1 篇 dynamic scenes
1 篇 differentiable r...
1 篇 gaussian splatti...
1 篇 depth refinement
1 篇 occupancy foreca...
1 篇 volume rendering
1 篇 neural rendering
1 篇 novel view synth...
1 篇 occupancy predic...
1 篇 human performanc...
1 篇 compact gaussian...
1 篇 mesh
1 篇 surface reconstr...
1 篇 depth completion

机构

13 篇 zhejiang univ pe...
12 篇 chinese univ hon...
10 篇 tech univ munich...
9 篇 univ hong kong p...
8 篇 shanghai ai lab ...
7 篇 univ sci & techn...
7 篇 shanghai jiao to...
7 篇 simon fraser uni...
7 篇 zhejiang univ st...
6 篇 univ toronto on
6 篇 smartmore people...
6 篇 tsinghua univ pe...
5 篇 carnegie mellon ...
5 篇 huawei noahs ark...
5 篇 lund univ lund
4 篇 peking univ peop...
4 篇 tsinghua univ sc...
4 篇 tsinghua univ de...
4 篇 wayne state univ...
4 篇 australian natl ...

作者

7 篇 zhou xiaowei
6 篇 niessner matthia...
6 篇 jia jiaya
6 篇 peng sida
6 篇 bao hujun
5 篇 tagliasacchi and...
5 篇 guibas leonidas
5 篇 liu yu-shen
4 篇 han xiaoguang
4 篇 yao ting
4 篇 qi xiaojuan
4 篇 dai angela
4 篇 theobalt christi...
4 篇 han zhizhong
4 篇 larsson viktor
3 篇 liu yuan
3 篇 lu cewu
3 篇 pollefeys marc
3 篇 chen yukang
3 篇 kim changil

语言

249 篇 英文

检索条件"主题词=3D from Multi-view and Sensors"

共 249 条记录，以下是81-90 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Planedepth: Self-supervised depth Estimation via Orthogonal Planes

PlaneDepth: Self-supervised Depth Estimation via Orthogonal ...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Ruoyu Yu, Zehao Gao, Shenghua ShanghaiTech Univ Shanghai Peoples R China Univ Tubingen Tubingen Germany Shanghai Engn Res Ctr Intelligent Vision & Imagin Shanghai Peoples R China Shanghai Engn Res Ctr Energy Efficient & Custom I Shanghai Peoples R China

ISBN: (纸本)9798350301298

multiple near frontal-parallel planes based depth representation demonstrated impressive results in self-supervised monocular depth estimation (MdE). Whereas, such a representation would cause the discontinuity of the ground as it is perpendicular to the frontal-parallel planes, which is detrimental to the identification of drivable space in autonomous driving. In this paper, we propose the Planedepth, a novel orthogonal planes based presentation, including vertical planes and ground planes. Planedepth estimates the depth distribution using a Laplacian Mixture Model based on orthogonal planes for an input image. These planes are used to synthesize a reference view to provide the self-supervision signal. Further, we find that the widely used resizing and cropping data augmentation breaks the orthogonality assumptions, leading to inferior plane predictions. We address this problem by explicitly constructing the resizing cropping transformation to rectify the predefined planes and predicted camera pose. Moreover, we propose an augmented self-distillation loss supervised with a bilateral occlusion mask to boost the robustness of orthogonal planes representation for occlusions. Thanks to our orthogonal planes representation, we can extract the ground plane in an unsupervised manner, which is important for autonomous driving. Extensive experiments on the KITTI dataset demonstrate the effectiveness and efficiency of our method. The code is available at https://***/svip-lab/Planedepth.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

Semidefinite Relaxations for Robust multiview Triangulation

Semidefinite Relaxations for Robust Multiview Triangulation

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haerenstam-Nielsen, Linus Zeller, Niclas Cremers, daniel Tech Univ Munich Munich Germany Munich Ctr Machine Learning Munich Germany Univ Oxford Oxford England Karlsruhe Univ Appl Sci Karlsruhe Germany

ISBN: (纸本)9798350301298

We propose an approach based on convex relaxations for certifiably optimal robust multiview triangulation. To this end, we extend existing relaxation approaches to non-robust multiview triangulation by incorporating a least squares cost function. We propose two formulations, one based on epipolar constraints and one based on fractional reprojection constraints. The first is lower dimensional and remains tight under moderate noise and outlier levels, while the second is higher dimensional and therefore slower but remains tight even under extreme noise and outlier levels. We demonstrate through extensive experiments that the proposed approaches allow us to compute provably optimal reconstructions even under significant noise and a large percentage of outliers.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

Self-Supervised 3d Scene Flow Estimation Guided by Superpoints

Self-Supervised 3D Scene Flow Estimation Guided by Superpoin...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shen, Yaqi Hui, Le Xie, Jin Yang, Jian Nanjing Univ Sci & Technol PCA Lab Nanjing Peoples R China

ISBN: (纸本)9798350301298

3d scene flow estimation aims to estimate point-wise motions between two consecutive frames of point clouds. Superpoints, i.e., points with similar geometric features, are usually employed to capture similar motions of local regions in 3d scenes for scene flow estimation. However, in existing methods, superpoints are generated with the offline clustering methods, which cannot characterize local regions with similar motions for complex 3d scenes well, leading to inaccurate scene flow estimation. To this end, we propose an iterative end-to-end superpoint based scene flow estimation framework, where the superpoints can be dynamically updated to guide the point-level flow prediction. Specifically, our framework consists of a flow guided superpoint generation module and a superpoint guided flow refinement module. In our superpoint generation module, we utilize the bidirectional flow information at the previous iteration to obtain the matching points of points and superpoint centers for soft point-to-superpoint association construction, in which the superpoints are generated for pairwise point clouds. With the generated superpoints, we first reconstruct the flow for each point by adaptively aggregating the superpoint-level flow, and then encode the consistency between the reconstructed flow of pairwise point clouds. Finally, we feed the consistency encoding along with the reconstructed flow into GRU to refine point-level flow. Extensive experiments on several different datasets show that our method can achieve promising performance. Code is available at https://***/supersyq/SPFlowNet.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

ObjectMatch: Robust Registration using Canonical Object Correspondences

ObjectMatch: Robust Registration using Canonical Object Corr...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Guemeli, Can dai, Angela Niessner, Matthias Tech Univ Munich Munich Germany

ISBN: (纸本)9798350301298

We present ObjectMatch(1), a semantic and object-centric camera pose estimator for RGB-d SLAM pipelines. Modern camera pose estimators rely on direct correspondences of overlapping regions between frames;however, they cannot align camera frames with little or no overlap. In this work, we propose to leverage indirect correspondences obtained via semantic object identification. For instance, when an object is seen from the front in one frame and from the back in another frame, we can provide additional pose constraints through canonical object correspondences. We first propose a neural network to predict such correspondences on a per-pixel level, which we then combine in our energy formulation with state-of-the-art keypoint matching solved with a joint Gauss-Newton optimization. In a pair-wise setting, our method improves registration recall of state-of-the-art feature matching, including from 24% to 45% in pairs with 10% or less inter-frame overlap. In registering RGB-d sequences, our method outperforms cutting-edge SLAM baselines in challenging, low-frame-rate scenarios, achieving more than 35% reduction in trajectory error in multiple scenes.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

Temporally Consistent Online depth Estimation Using Point-Based Fusion

Temporally Consistent Online Depth Estimation Using Point-Ba...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Khan, Numair Penner, Eric Lanman, douglas Xiao, Lei

ISBN: (纸本)9798350301298

depth estimation is an important step in many computer vision problems such as 3d reconstruction, novel view synthesis, and computational photography. Most existing work focuses on depth estimation from single frames. When applied to videos, the result lacks temporal consistency, showing flickering and swimming artifacts. In this paper we aim to estimate temporally consistent depth maps of video streams in an online setting. This is a difficult problem as future frames are not available and the method must choose between enforcing consistency and correcting errors from previous estimations. The presence of dynamic objects further complicates the problem. We propose to address these challenges by using a global point cloud that is dynamically updated each frame, along with a learned fusion approach in image space. Our approach encourages consistency while simultaneously allowing updates to handle errors and dynamic objects. Qualitative and quantitative results show that our method achieves state-of-the-art quality for consistent video depth estimation.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

Learning Optical Expansion from Scale Matching

Learning Optical Expansion from Scale Matching

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ling, Han Sun, Yinghui Sun, Quansen Ren, Zhenwen Nanjing Univ Sci & Technol Nanjing Peoples R China Southwest Univ Sci & Technol Mianyang Peoples R China SongShan Lab Dongguan Peoples R China

ISBN: (纸本)9798350301298

This paper address the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3d vision tasks. Previous methods estimate optical expansion mainly from optical flow results, but this two-stage architecture makes their results limited by the accuracy of optical flow and less robust. To solve these problems, we propose the concept of 3d optical flow by integrating optical expansion into the 2d optical flow, which is implemented by a plug-and-play module, namely TPCV. TPCV implements matching features at the correct location and scale, thus allowing the simultaneous optimization of optical flow and optical expansion tasks. Experimentally, we apply TPCV to the RAFT optical flow baseline. Experimental results show that the baseline optical flow performance is substantially improved. Moreover, we apply the optical flow and optical expansion results to various dynamic 3d vision tasks, including motion-in-depth, time-to-collision, and scene flow, often achieving significant improvement over the prior SOTA. Code is available at https://***/HanLingsgjk/TPCV.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

ARO-Net: Learning Implicit Fields from Anchored Radial Observations

ARO-Net: Learning Implicit Fields from Anchored Radial Obser...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Yizhi Huang, Zeyu Sharnir, Ariel Huang, Hui Zhang, Hao Hu, Ruizhen Shenzhen Univ Shenzhen Peoples R China Simon Fraser Univ Burnaby BC Canada Reichman Univ Herzliyya Israel

ISBN: (纸本)9798350301298

We introduce anchored radial observations (ARO), a novel shape encoding for learning implicit field representation of 3d shapes that is category-agnostic and generalizable amid significant shape variations. The main idea behind our work is to reason about shapes through partial observations from a set of viewpoints, called anchors. We develop a general and unified shape representation by employing a fixed set of anchors, via Fibonacci sampling, and designing a coordinatebased deep neural network to predict the occupancy value of a query point in space. differently from prior neural implicit models that use global shape feature, our shape encoder operates on contextual, query-specific features. To predict point occupancy, locally observed shape information from the perspective of the anchors surrounding the input query point are encoded and aggregated through an attention module, before implicit decoding is performed. We demonstrate the quality and generality of our network, coined ARO-Net, on surface reconstruction from sparse point clouds, with tests on novel and unseen object categories, "one-shape" training, and comparisons to state-of-the-art neural and classical methods for reconstruction and tessellation.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

TriVol: Point Cloud Rendering via Triple Volumes

TriVol: Point Cloud Rendering via Triple Volumes

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hu, Tao Xu, Xiaogang Chu, Ruihang Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China SmartMore Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Existing learning-based methods for point cloud rendering adopt various 3d representations and feature querying mechanisms to alleviate the sparsity problem of point clouds. However, artifacts still appear in rendered images, due to the challenges in extracting continuous and discriminative 3d features from point clouds. In this paper, we present a dense while lightweight 3d representation, named TriVol, that can be combined with NeRF to render photorealistic images from point clouds. Our TriVol consists of triple slim volumes, each of which is encoded from the point cloud. TriVol has two advantages. First, it fuses respective fields at different scales and thus extracts local and nonlocal features for discriminative representation. Second, since the volume size is greatly reduced, our 3d decoder can be efficiently inferred, allowing us to increase the resolution of the 3d space to render more point details. Extensive experiments on different benchmarks with varying kinds of scenes/objects demonstrate our framework's effectiveness compared with current approaches. Moreover, our framework has excellent generalization ability to render a category of scenes/objects without fine-tuning. The source code is available at https://***/dvlabresearch/***.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

3d shape reconstruction of semi-transparent worms

3D shape reconstruction of semi-transparent worms

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ilett, Thomas P. Yuval, Omer Ranner, Thomas Cohen, Netta Hogg, david C. Univ Leeds Leeds W Yorkshire England

ISBN: (纸本)9798350301298

3d shape reconstruction typically requires identifying object features or textures in multiple images of a subject. This approach is not viable when the subject is semi-transparent and moving in and out of focus. Here we overcome these challenges by rendering a candidate shape with adaptive blurring and transparency for comparison with the images. We use the microscopic nematode Caenorhabditis elegans as a case study as it freely explores a 3d complex fluid with constantly changing optical properties. We model the slender worm as a 3d curve using an intrinsic parametrisation that naturally admits biologically-informed constraints and regularisation. To account for the changing optics we develop a novel differentiable renderer to construct images from 2d projections and compare against raw images to generate a pixel-wise error to jointly update the curve, camera and renderer parameters using gradient descent. The method is robust to interference such as bubbles and dirt trapped in the fluid, stays consistent through complex sequences of postures, recovers reliable estimates from blurry images and provides a significant improvement on previous attempts to track C. elegans in 3d. Our results demonstrate the potential of direct approaches to shape estimation in complex physical environments in the absence of ground-truth data.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

Towards Better Gradient Consistency for Neural Signed distance Functions via Level Set Alignment

Towards Better Gradient Consistency for Neural Signed Distan...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ma, Baorui Zhou, Junsheng Liu, Yu-Shen Han, Zhizhong Tsinghua Univ Sch Software BNRist Beijing Peoples R China Wayne State Univ Dept Comp Sci Detroit MI USA

ISBN: (纸本)9798350301298

Neural signed distance functions (SdFs) have shown remarkable capability in representing geometry with details. However, without signed distance supervision, it is still a challenge to infer SdFs from point clouds or multi-view images using neural networks. In this paper, we claim that gradient consistency in the field, indicated by the parallelism of level sets, is the key factor affecting the inference accuracy. Hence, we propose a level set alignment loss to evaluate the parallelism of level sets, which can be minimized to achieve better gradient consistency. Our novelty lies in that we can align all level sets to the zero level set by constraining gradients at queries and their projections on the zero level set in an adaptive way. Our insight is to propagate the zero level set to everywhere in the field through consistent gradients to eliminate uncertainty in the field that is caused by the discreteness of 3d point clouds or the lack of observations from multi-view images. Our proposed loss is a general term which can be used upon different methods to infer SdFs from 3d point clouds and multi-view images. Our numerical and visual comparisons demonstrate that our loss can significantly improve the accuracy of SdFs inferred from point clouds or multi-view images under various benchmarks. Code and data are available at https://***/mabaorui/TowardsBetterGradient.

关键词： 3d from multi-view and sensors

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共25页 << < 5 6 7 8 9 10 11 12 13 14 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：