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检索条件"主题词=3D from multi-view and sensors"

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ORCa: Glossy Objects as Radiance-Field Cameras

ORCa: Glossy Objects as Radiance-Field Cameras

引用

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

作者： Tiwary, Kushagra dave, Akshat Behari, Nikhil Klinghoffer, Tzofi Veeraraghavan, Ashok Raskar, Ramesh MIT Cambridge MA 02139 USA Rice Univ Houston TX 77251 USA

ISBN: (纸本)9798350301298

Reflections on glossy objects contain valuable and hidden information about the surrounding environment. By converting these objects into cameras, we can unlock exciting applications, including imaging beyond the camera's field-of-view and from seemingly impossible vantage points, e.g. from reflections on the human eye. However, this task is challenging because reflections depend jointly on object geometry, material properties, the 3d environment, and the observer's viewing direction. Our approach converts glossy objects with unknown geometry into radiance-field cameras to image the world from the object's perspective. Our key insight is to convert the object surface into a virtual sensor that captures cast reflections as a 2d projection of the 5d environment radiance field visible to and surrounding the object. We show that recovering the environment radiance fields enables depth and radiance estimation from the object to its surroundings in addition to beyond field-of-view novel-view synthesis, i.e. rendering of novel views that are only directly visible to the glossy object present in the scene, but not the observer. Moreover, using the radiance field we can image around occluders caused by close-by objects in the scene. Our method is trained end-to-end on multi-view images of the object and jointly estimates object geometry, diffuse radiance, and the 5d environment radiance field. For more information, visit our website.

关键词： 3d from multi-view and sensors

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GANHead: Towards Generative Animatable Neural Head Avatars

GANHead: Towards Generative Animatable Neural Head Avatars

引用

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

作者： Wu, Sijing Yan, Yichao Li, Yunhao Cheng, Yuhao Zhu, Wenhan Gao, Ke Li, Xiaobo Zhai, Guangtao Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798350301298

To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Generative Animatable Neural Head Avatar), a novel generative head model that takes advantages of both the fine-grained control over the explicit expression parameters and the realistic rendering results of implicit representations. Specifically, GANHead represents coarse geometry, fine-gained details and texture via three networks in canonical space to obtain the ability to generate complete and realistic head avatars. To achieve flexible animation, we define the deformation filed by standard linear blend skinning (LBS), with the learned continuous pose and expression bases and LBS weights. This allows the avatars to be directly animated by FLAME [22] parameters and generalize well to unseen poses and expressions. Compared to state-of-the-art (SOTA) methods, GANHead achieves superior performance on head avatar generation and raw scan fitting.

关键词： 3d from multi-view and sensors

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A Practical Stereo depth System for Smart Glasses

A Practical Stereo Depth System for Smart Glasses

引用

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

作者： Wang, Jialiang Scharstein, daniel Bapat, Akash Blackburn-Matzen, Kevin Yu, Matthew Lehman, Jonathan Alsisan, Suhib Wang, Yanghan Tsai, Sam Frahm, Jan-Michael He, Zijian Vajda, Peter Cohen, Michael F. Uyttendaele, Matt Meta Platforms Inc Menlo Pk CA 94025 USA Adobe Seattle WA USA

ISBN: (纸本)9798350301298

We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable. The output of our depth sensing system is then used in a novel view generation pipeline to create 3d computational photography effects using point-of-view images captured by smart glasses. All these steps are executed on-device on the stringent compute budget of a mobile phone, and because we expect the users can use a wide range of smartphones, our design needs to be general and cannot be dependent on a particular hardware or ML accelerator such as a smartphone GPU. Although each of these steps is well studied, a description of a practical system is still lacking. For such a system, all these steps need to work in tandem with one another and fallback gracefully on failures within the system or less than ideal input data. We show how we handle unforeseen changes to calibration, e.g., due to heat, robustly support depth estimation in the wild, and still abide by the memory and latency constraints required for a smooth user experience. We show that our trained models are fast, and run in less than 1s on a six-year-old Samsung Galaxy S8 phone's CPU. Our models generalize well to unseen data and achieve good results on Middlebury and in-the-wild images captured from the smart glasses.

关键词： 3d from multi-view and sensors

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Robust multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting

Robust Multiview Point Cloud Registration with Reliable Pose...

引用

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

作者： Wang, Haiping Liu, Yuan dong, Zhen Guo, Yulan Liu, Yu-Shen Wang, Wenping Yang, Bisheng Wuhan Univ Wuhan Hubei Peoples R China Univ Hong Kong Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Tsinghua Univ Beijing Peoples R China Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9798350301298

In this paper, we present a new method for the multiview registration of point cloud. Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses. However, constructing a densely-connected graph is time-consuming and contains lots of outlier edges, which makes the subsequent IRLS struggle to find correct poses. To address the above problems, we first propose to use a neural network to estimate the overlap between scan pairs, which enables us to construct a sparse but reliable pose graph. Then, we design a novel history reweighting function in the IRLS scheme, which has strong robustness to outlier edges on the graph. In comparison with existing multiview registration methods, our method achieves 11% higher registration recall on the 3dMatch dataset and similar to 13% lower registration errors on the ScanNet dataset while reducing similar to 70% required pairwise registrations. Comprehensive ablation studies are conducted to demonstrate the effectiveness of our designs. The source code is available at https://***/WHUUSI3dV/SGHR.

关键词： 3d from multi-view and sensors

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multi-Space Neural Radiance Fields

Multi-Space Neural Radiance Fields

引用

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

作者： Yin, Ze-Xin Qiu, Jiaxiong Cheng, Ming-Ming Ren, Bo Nankai Univ VCIP CS Tianjin Peoples R China

ISBN: (纸本)9798350301298

Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field, we propose a multi-space neural radiance field (MS-NeRF) that represents the scene using a group of feature fields in parallel sub-spaces, which leads to a better understanding of the neural network toward the existence of reflective and refractive objects. Our multi-space scheme works as an enhancement to existing NeRF methods, with only small computational overheads needed for training and inferring the extra-space outputs. We demonstrate the superiority and compatibility of our approach using three representative NeRF-based models, i.e., NeRF, Mip-NeRF, and Mip-NeRF 360. Comparisons are performed on a novelly constructed dataset consisting of 25 synthetic scenes and 7 real captured scenes with complex reflection and refraction, all having 360-degree viewpoints. Extensive experiments show that our approach significantly outperforms the existing single-space NeRF methods for rendering high-quality scenes concerned with complex light paths through mirror-like objects. Our code and dataset will be publicly available at https://***/msnerf.

关键词： 3d from multi-view and sensors

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RobustNeRF: Ignoring distractors with Robust Losses

RobustNeRF: Ignoring Distractors with Robust Losses

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

作者： Sabour, Sara Vora, Suhani duckworth, daniel Krasin, Ivan Fleet, david J. Tagliasacchi, Andrea Google Res Brain Team Mountain View CA 94043 USA Univ Toronto Toronto ON Canada Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9798350301298

Neural radiance fields (NeRF) excel at synthesizing new views given multi-view, calibrated images of a static scene. When scenes include distractors, which are not persistent during image capture (moving objects, lighting variations, shadows), artifacts appear as view-dependent effects or 'floaters'. To cope with distractors, we advocate a form of robust estimation for NeRF training, modeling distractors in training data as outliers of an optimization problem. Our method successfully removes outliers from a scene and improves upon our baselines, on synthetic and real-world scenes. Our technique is simple to incorporate in modern NeRF frameworks, with few hyper-parameters. It does not assume a priori knowledge of the types of distractors, and is instead focused on the optimization problem rather than pre-processing or modeling transient objects. More results at https://***/public.

关键词： 3d from multi-view and sensors

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SPIn-NeRF: multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields

SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting ...

引用

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

作者： Mirzaei, Ashkan Aumentado-Armstrong, Tristan derpanis, Konstantinos G. Kelly, Jonathan Brubaker, Marcus A. Gilitschenski, Igor Levinshtein, Alex Samsung AI Ctr Toronto Toronto ON Canada Univ Toronto Toronto ON Canada York Univ N York ON Canada Vector Inst AI Toronto ON Canada

ISBN: (纸本)9798350301298

Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3d scene, such that the replaced region is visually plausible and consistent with its context. We refer to this task as 3d inpainting. In 3d, solutions must be both consistent across multiple views and geometrically valid. In this paper, we propose a novel 3d inpainting method that addresses these challenges. Given a small set of posed images and sparse annotations in a single input image, our framework first rapidly obtains a 3d segmentation mask for a target object. Using the mask, a perceptual optimization-based approach is then introduced that leverages learned 2d image inpainters, distilling their information into 3d space, while ensuring view consistency. We also address the lack of a diverse benchmark for evaluating 3d scene inpainting methods by introducing a dataset comprised of challenging real-world scenes. In particular, our dataset contains views of the same scene with and without a target object, enabling more principled benchmarking of the 3d inpainting task. We first demonstrate the superiority of our approach on multiview segmentation, comparing to NeRF-based methods and 2d segmentation approaches. We then evaluate on the task of 3d inpainting, establishing state-of-the-art performance against other NeRF manipulation algorithms, as well as a strong 2d image inpainter baseline.

关键词： 3d from multi-view and sensors

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Instant Volumetric Head Avatars

Instant Volumetric Head Avatars

引用

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

作者： Zielonka, Wojciech Bolkart, Timo Thies, Justus Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (纸本)9798350301298

We present Instant Volumetric Head Avatars (INSTA), a novel approach for reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic neural radiance field based on neural graphics primitives embedded around a parametric face model. Our pipeline is trained on a single monocular RGB portrait video that observes the subject under different expressions and views. While state-of-the-art methods take up to several days to train an avatar, our method can reconstruct a digital avatar in less than 10 minutes on modern GPU hardware, which is orders of magnitude faster than previous solutions. In addition, it allows for the interactive rendering of novel poses and expressions. By leveraging the geometry prior of the underlying parametric face model, we demonstrate that INSTA extrapolates to unseen poses. In quantitative and qualitative studies on various subjects, INSTA outperforms state-of-the-art methods regarding rendering quality and training time. Project website: https://***/insta/

关键词： 3d from multi-view and sensors

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RUST: Latent Neural Scene Representations from Unposed Imagery

RUST: Latent Neural Scene Representations from Unposed Image...

引用

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

作者： Sajjadi, Mehdi S. M. Mahendran, Aravindh Kipf, Thomas Pot, Etienne duckworth, daniel Lucic, Mario Greff, Klaus Google Res Brain Team Santa Clara CA 95113 USA

ISBN: (纸本)9798350301298

Inferring the structure of 3d scenes from 2d observations is a fundamental challenge in computer vision. Recently popularized approaches based on neural scene representations have achieved tremendous impact and have been applied across a variety of applications. One of the major remaining challenges in this space is training a single model which can provide latent representations which effectively generalize beyond a single scene. Scene Representation Transformer (SRT) has shown promise in this direction, but scaling it to a larger set of diverse scenes is challenging and necessitates accurately posed ground truth data. To address this problem, we propose RUST (Really Unposed Scene representation Transformer), a pose-free approach to novel view synthesis trained on RGB images alone. Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis. We perform an empirical investigation into the learned latent pose structure and show that it allows meaningful test-time camera transformations and accurate explicit pose readouts. Perhaps surprisingly, RUST achieves similar quality as methods which have access to perfect camera pose, thereby unlocking the potential for large-scale training of amortized neural scene representations.

关键词： 3d from multi-view and sensors

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Sphere-Guided Training of Neural Implicit Surfaces

Sphere-Guided Training of Neural Implicit Surfaces

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

作者： dogaru, Andreea Ardelean, Andrei Timotei Ignatyev, Savva Zakharov, Egor Burnaev, Evgeny Skolkovo Inst Sci & Technol Moscow Russia Friedrich Alexander Univ Erlangen Nurnberg Erlangen Germany Artificial Intelligence Res Inst Kaiserslautern Germany

ISBN: (纸本)9798350301298

In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3d reconstruction. These methods, however, apply the ray marching procedure for the entire scene volume, leading to reduced sampling efficiency and, as a result, lower reconstruction quality in the areas of high-frequency details. In this work, we address this problem via joint training of the implicit function and our new coarse sphere-based surface reconstruction. We use the coarse representation to efficiently exclude the empty volume of the scene from the volumetric ray marching procedure without additional forward passes of the neural surface network, which leads to an increased fidelity of the reconstructions compared to the base systems. We evaluate our approach by incorporating it into the training procedures of several implicit surface modeling methods and observe uniform improvements across both synthetic and real-world datasets. Our codebase can be accessed via the project page

关键词： 3d from multi-view and sensors

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