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

共 249 条记录，以下是181-190 订阅

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Self-supervised Pre-training with Masked Shape Prediction for 3d Scene Understanding

Self-supervised Pre-training with Masked Shape Prediction fo...

引用

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

作者： Jiang, Li Yang, Zetong Shi, Shaoshuai Golyanik, Vladislav dai, dengxin Schiele, Bernt Saarland Informatics Campus Max Planck Inst Informat Saarbrucken Germany CUHK Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Masked signal modeling has greatly advanced self-supervised pre-training for language and 2d images. However, it is still not fully explored in 3d scene understanding. Thus, this paper introduces Masked Shape Prediction (MSP), a new framework to conduct masked signal modeling in 3d scenes. MSP uses the essential 3d semantic cue, i.e., geometric shape, as the prediction target for masked points. The context-enhanced shape target consisting of explicit shape context and implicit deep shape feature is proposed to facilitate exploiting contextual cues in shape prediction. Meanwhile, the pre-training architecture in MSP is carefully designed to alleviate the masked shape leakage from point coordinates. Experiments on multiple 3d understanding tasks on both indoor and outdoor datasets demonstrate the effectiveness of MSP in learning good feature representations to consistently boost downstream performance.

关键词： 3d from multi-view and sensors

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Robust dynamic Radiance Fields

Robust Dynamic Radiance Fields

引用

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

作者： Liu, Yu-Lun Gao, Chen Meuleman, Andreas Tseng, Hung-Yu Saraf, Ayush Kim, Changil Chuang, Yung-Yu Kopf, Johannes Huang, Jia-Bin Meta Menlo Pk CA 94025 USA Natl Taiwan Univ Taipei Taiwan Korea Adv Inst Sci & Technol Daejeon South Korea Univ Maryland College Pk MD USA

ISBN: (纸本)9798350301298

dynamic radiance field reconstruction methods aim to model the time-varying structure and appearance of a dynamic scene. Existing methods, however, assume that accurate camera poses can be reliably estimated by Structure from Motion (SfM) algorithms. These methods, thus, are unreliable as SfM algorithms often fail or produce erroneous poses on challenging videos with highly dynamic objects, poorly textured surfaces, and rotating camera motion. We address this robustness issue by jointly estimating the static and dynamic radiance fields along with the camera parameters (poses and focal length). We demonstrate the robustness of our approach via extensive quantitative and qualitative experiments. Our results show favorable performance over the state-of-the-art dynamic view synthesis methods.

关键词： 3d from multi-view and sensors

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dKM: dense Kernelized Feature Matching for Geometry Estimation

DKM: Dense Kernelized Feature Matching for Geometry Estimati...

引用

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

作者： Edstedt, Johan Athanasiadis, Ioannis Wadenback, Marten Felsberg, Michael Linkoping Univ Comp Vis Lab Linkoping Sweden

ISBN: (纸本)9798350301298

Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3d scene. In this paper we consider the dense approach instead of the more common sparse paradigm, thus striving to find all correspondences. Perhaps counter-intuitively, dense methods have previously shown inferior performance to their sparse and semi-sparse counterparts for estimation of two-view geometry. This changes with our novel dense method, which outperforms both dense and sparse methods on geometry estimation. The novelty is threefold: First, we propose a kernel regression global matcher. Secondly, we propose warp refinement through stacked feature maps and depthwise convolution kernels. Thirdly, we propose learning dense confidence through consistent depth and a balanced sampling approach for dense confidence maps. Through extensive experiments we confirm that our proposed dense method, dense Kernelized Feature Matching, sets a new state-of-the-art on multiple geometry estimation benchmarks. In particular, we achieve an improvement on Megadepth-1500 of +4.9 and +8.9 AUC@5. compared to the best previous sparse method and dense method respectively. Our code is provided at the following repository: https://***/Parskatt/dKM.

关键词： 3d from multi-view and sensors

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Masked representation learning for domain generalized stereo matching

Masked representation learning for domain generalized stereo...

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

作者： Rao, Zhibo Xiong, Bangshu He, Mingyi dai, Yuchao He, Renjie Shen, Zhelun Li, Xing Nanchang Hangkong Univ Nanchang Peoples R China Northwestern Polytech Univ Xian Peoples R China Baidu Res Beijing Peoples R China

ISBN: (纸本)9798350301298

Recently, many deep stereo matching methods have begun to focus on cross-domain performance, achieving impressive achievements. However, these methods did not deal with the significant volatility of generalization performance among different training epochs. Inspired by masked representation learning and multi-task learning, this paper designs a simple and effective masked representation for domain generalized stereo matching. First, we feed the masked left and complete right images as input into the models. Then, we add a lightweight and simple decoder following the feature extraction module to recover the original left image. Finally, we train the models with two tasks (stereo matching and image reconstruction) as a pseudo-multi-task learning framework, promoting models to learn structure information and to improve generalization performance. We implement our method on two well-known architectures (CFNet and LacGwcNet) to demonstrate its effectiveness. Experimental results on multi-datasets show that: (1) our method can be easily plugged into the current various stereo matching models to improve generalization performance;(2) our method can reduce the significant volatility of generalization performance among different training epochs;(3) we find that the current methods prefer to choose the best results among different training epochs as generalization performance, but it is impossible to select the best performance by ground truth in practice.

关键词： 3d from multi-view and sensors

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dynIBaR: Neural dynamic Image-Based Rendering

DynIBaR: Neural Dynamic Image-Based Rendering

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

作者： Li, Zhengqi Wang, Qianqian Cole, Forrester Tucker, Richard Snavely, Noah Google Res Mountain View CA 94043 USA Cornell Tech New York NY USA

ISBN: (纸本)9798350301298

We address the problem of synthesizing novel views from a monocular video depicting a complex dynamic scene. State-of-the-art methods based on temporally varying Neural Radiance Fields (aka dynamic NeRFs) have shown impressive results on this task. However, for long videos with complex object motions and uncontrolled camera trajectories, these methods can produce blurry or inaccurate renderings, hampering their use in real-world applications. Instead of encoding the entire dynamic scene within the weights of MLPs, we present a new approach that addresses these limitations by adopting a volumetric image-based rendering framework that synthesizes new viewpoints by aggregating features from nearby views in a scene motion-aware manner. Our system retains the advantages of prior methods in its ability to model complex scenes and view-dependent effects, but also enables synthesizing photo-realistic novel views from long videos featuring complex scene dynamics with unconstrained camera trajectories. We demonstrate significant improvements over state-of-the-art methods on dynamic scene datasets, and also apply our approach to in-the-wild videos with challenging camera and object motion, where prior methods fail to produce high-quality renderings.

关键词： 3d from multi-view and sensors

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TMO: Textured Mesh Acquisition of Objects with a Mobile device by using differentiable Rendering

TMO: Textured Mesh Acquisition of Objects with a Mobile Devi...

引用

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

作者： Choi, Jaehoon Jung, dongki Lee, Taejae Kim, Sangwook Jung, Youngdong Manocha, dinesh Lee, donghwan NAVER LABS Seongnam South Korea Univ Maryland College Pk MD USA

ISBN: (纸本)9798350301298

We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBd-aided structure from motion, which can yield filtered depth maps and refines camera poses guided by corresponding depth. Then, we adopt the neural implicit surface reconstruction method, which allows for high-quality mesh and develops a new training process for applying a regularization provided by classical multi-view stereo methods. Moreover, we apply a differentiable rendering to fine-tune incomplete texture maps and generate textures which are perceptually closer to the original scene. Our pipeline can be applied to any common objects in the real world without the need for either in-the-lab environments or accurate mask images. We demonstrate results of captured objects with complex shapes and validate our method numerically against existing 3d reconstruction and texture mapping methods.

关键词： 3d from multi-view and sensors

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Local-to-Global Registration for Bundle-Adjusting Neural Radiance Fields

Local-to-Global Registration for Bundle-Adjusting Neural Rad...

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

作者： Chen, Yue Chen, Xingyu Wang, Xuan Zhang, Qi Guo, Yu Shan, Ying Wang, Fci Natl Key Lab Human Machine Hybrid Augmented Intel Xian Peoples R China Xi An Jiao Tong Univ IAIR Xian Peoples R China Ant Grp Hangzhou Peoples R China Tencent AI Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Neural Radiance Fields (NeRF) have achieved photorealistic novel views synthesis;however, the requirement of accurate camera poses limits its application. despite analysis-by-synthesis extensions for jointly learning neural 3d representations and registering camera frames exist, they are susceptible to suboptimal solutions if poorly initialized. We propose L2G-NeRF, a Local-to-Global registration method for bundle-adjusting Neural Radiance Fields: first, a pixel-wise flexible alignment, followed by a framewise constrained parametric alignment. Pixel-wise local alignment is learned in an unsupervised way via a deep network which optimizes photometric reconstruction errors. Frame-wise global alignment is performed using differentiable parameter estimation solvers on the pixel-wise correspondences to find a global transformation. Experiments on synthetic and real-world data show that our method outperforms the current state-of-the-art in terms of high-fidelity reconstruction and resolving large camera pose misalignment. Our module is an easy-to-use plugin that can be applied to NeRF variants and other neural field applications. The Code and supplementary materials are available at https://***/L2G-NeRF/.

关键词： 3d from multi-view and sensors

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NeAT: Learning Neural Implicit Surfaces with Arbitrary Topologies from multi-view Images

NeAT: Learning Neural Implicit Surfaces with Arbitrary Topol...

引用

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

作者： Meng, Xiaoxu Chen, Weikai Yang, Bo Tencent Games Digital Content Technol Ctr Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Recent progress in neural implicit functions has set new state-of-the-art in reconstructing high-fidelity 3d shapes from a collection of images. However, these approaches are limited to closed surfaces as they require the surface to be represented by a signed distance field. In this paper, we propose NeAT, a new neural rendering framework that can learn implicit surfaces with arbitrary topologies from multi-view images. In particular, NeAT represents the 3d surface as a level set of a signed distance function (SdF) with a validity branch for estimating the surface existence probability at the query positions. We also develop a novel neural volume rendering method, which uses SdF and validity to calculate the volume opacity and avoids rendering points with low validity. NeAT supports easy field-to-mesh conversion using the classic Marching Cubes algorithm. Extensive experiments on dTU [20], MGN [4], and deep Fashion 3d [19] datasets indicate that our approach is able to faithfully reconstruct both watertight and non-watertight surfaces. In particular, NeAT significantly outperforms the state-of-the-art methods in the task of open surface reconstruction both quantitatively and qualitatively.

关键词： 3d from multi-view and sensors

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Revisiting the P3P Problem

Revisiting the P3P Problem

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

作者： ding, Yaqing Yang, Jian Larsson, Viktor Olsson, Carl Astrom, Kalle Lund Univ Lund Sweden Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9798350301298

One of the classical multi-view geometry problems is the so called P3P problem, where the absolute pose of a calibrated camera is determined from three 2d-to-3d correspondences. Since these solvers form a critical component of many vision systems (e.g. in localization and Structure-from-Motion), there have been significant effort in developing faster and more stable algorithms. While the current state-of-the-art solvers are both extremely fast and stable, there still exist configurations where they break down. In this paper we algebraically formulate the problem as finding the intersection of two conics. With this formulation we are able to analytically characterize the real roots of the polynomial system and employ a tailored solution strategy for each problem instance. The result is a fast and stable solver, that is able to correctly solve cases where competing methods might fail. Our experimental evaluation shows that we outperform the current state-of-the-art methods both in terms of speed and success rate.

关键词： 3d from multi-view and sensors

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diffusioNeRF: Regularizing Neural Radiance Fields with denoising diffusion Models

DiffusioNeRF: Regularizing Neural Radiance Fields with Denoi...

引用

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

作者： Wynn, Jamie Turmukhambetov, daniyar Niantic San Francisco CA 94111 USA

ISBN: (纸本)9798350301298

Under good conditions, Neural Radiance Fields (NeRFs) have shown impressive results on novel view synthesis tasks. NeRFs learn a scene's color and density fields by minimizing the photometric discrepancy between training views and differentiable renderings of the scene. Once trained from a sufficient set of views, NeRFs can generate novel views from arbitrary camera positions. However, the scene geometry and color fields are severely under-constrained, which can lead to artifacts, especially when trained with few input views. To alleviate this problem we learn a prior over scene geometry and color, using a denoising diffusion model (ddM). Our ddM is trained on RGBd patches of the synthetic Hypersim dataset and can be used to predict the gradient of the logarithm of a joint probability distribution of color and depth patches. We show that, these gradients of logarithms of RGBd patch priors serve to regularize geometry and color of a scene. during NeRF training, random RGBd patches are rendered and the estimated gradient of the log-likelihood is backpropagated to the color and density fields. Evaluations on LLFF, the most relevant dataset, show that our learned prior achieves improved quality in the reconstructed geometry and improved generalization to novel views. Evaluations on dTU show improved reconstruction quality among NeRF methods.

关键词： 3d from multi-view and sensors

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