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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Steady-state Non-Line-of-Sight Imaging 32

Steady-state Non-Line-of-Sight Imaging

引用

32nd IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chen, Wenzheng Daneau, Simon Mannan, Fahim Heide, Felix Algolux Montreal PQ Canada Univ Toronto Toronto ON Canada Univ Montreal Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781728132938

Conventional intensity cameras recover objects in the direct line-of-sight of the camera, whereas occluded scene parts are considered lost in this process. Non-line-of-sight imaging (NLOS) aims at recovering these occluded objects by analyzing their indirect reflections on visible scene surfaces. Existing NLOS methods temporally probe the indirect light transport to unmix light paths based on their travel time, which mandates specialized instrumentation that suffers from low photon efficiency, high cost, and mechanical scanning. We depart from temporal probing and demonstrate steady-state NLOS imaging using conventional intensity sensors and continuous illumination. Instead of assuming perfectly isotropic scattering, the proposed method exploits directionality in the hidden surface reflectance, resulting in (small) spatial variation of their indirect reflections for varying illumination. To tackle the shape-dependence of these variations, we propose a trainable architecture which learns to map diffuse indirect reflections to scene reflectance using only synthetic training data. Relying on consumer color image sensors, with high fill factor, high quantum efficiency and low read-out noise, we demonstrate high-fidelity color NLOS imaging for scene configurations tackled before with picosecond time resolution.

关键词： Computational Photography Low-level vision Physics-based vision and Shape-from-X

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Style-based Point Generator with Adversarial Rendering for Point Cloud Completion

Style-based Point Generator with Adversarial Rendering for P...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xie, Chulin Wang, Chuxin Zhang, Bo Yang, Hao Chen, Dong Wen, Fang Univ Illinois Champaign IL 61820 USA Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Asia Beijing Peoples R China

ISBN: (纸本)9781665445092

In this paper, we proposed a novel Style-based Point Generator with Adversarial Rendering (SpareNet) for point cloud completion. Firstly, we present the channel-attentive EdgeConv to fully exploit the local structures as well as the global shape in point features. Secondly, we observe that the concatenation manner used by vanilla foldings limits its potential of generating a complex and faithful shape. Enlightened by the success of StyleGAN, we regard the shape feature as style code that modulates the normalization layers during the folding, which considerably enhances its capability. Thirdly, we realize that existing point supervisions, e.g., Chamfer Distance or Earth Mover's Distance, cannot faithfully reflect the perceptual quality of the reconstructed points. To address this, we propose to project the completed points to depth maps with a differentiable renderer and apply adversarial training to advocate the perceptual realism under different viewpoints. Comprehensive experiments on ShapeNet and KITTI prove the effectiveness of our Method, which achieves state-of the-art quantitative performance while offering superior visual quality.

关键词： Training Earth Visualization Shape Image edge detection Rendering (computer graphics) Feature extraction

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A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift

A Differentiable Two-stage Alignment Scheme for Burst Image ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Guo, Shi Yang, Xi Ma, Jianqi Ren, Gaofeng Zhang, Lei Hong Kong Polytech Univ Hong Kong Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Denoising and demosaicking are two essential steps to reconstruct a clean full-color image from the raw data. Recently, joint denoising and demosaicking (JDD) for burst images, namely JDD-B, has attracted much attention by using multiple raw images captured in a short time to reconstruct a single high-quality image. One key challenge of JDD-B lies in the robust alignment of image frames. State-of-the-art alignment methods in feature domain cannot effectively utilize the temporal information of burst images, where large shifts commonly exist due to camera and object motion. In addition, the higher resolution (e.g., 4K) of modern imaging devices results in larger displacement between frames. To address these challenges, we design a differentiable two-stage alignment scheme sequentially in patch and pixel level for effective JDD-B. The input burst images are firstly aligned in the patch level by using a differentiable progressive block matching method, which can estimate the offset between distant frames with small computational cost. Then we perform implicit pixel-wise alignment in full-resolution feature domain to refine the alignment results. The two stages are jointly trained in an end-to-end manner. Extensive experiments demonstrate the significant improvement of our method over existing JDD-B methods. Codes are available https://***/GuoShi28/2StageAlign.

关键词： Visualization computer vision Image resolution Codes Noise reduction Computational efficiency pattern recognition

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NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

NICE-SLAM: Neural Implicit Scalable Encoding for SLAM

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhu, Zihan Peng, Songyou Larsson, Viktor Xu, Weiwei Bao, Hujun Cui, Zhaopeng Oswald, Martin R. Pollefeys, Marc Zhejiang Univ State Key Lab CAD & CG Hangzhou Zhejiang Peoples R China Swiss Fed Inst Technol Zurich Switzerland Lund Univ Lund Sweden MPI Intelligent Syst Tubingen Germany Univ Amsterdam Amsterdam Netherlands Microsoft Redmond WA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Neural implicit representations have recently shown encouraging results in various domains, including promising progress in simultaneous localization and mapping (SLAM). Nevertheless, existing methods produce over-smoothed scene reconstructions and have difficulty scaling up to large scenes. These limitations are mainly due to their simple fully-connected network architecture that does not incorporate local information in the observations. In this paper, we present NICE-SLAM, a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation. Optimizing this representation with pre-trained geometric priors enables detailed reconstruction on large indoor scenes. Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust. Experiments on five challenging datasets demonstrate competitive results of NICE-SLAM in both mapping and tracking quality. Project page: https://***/nice-slam.

关键词： Geometry Visualization computer vision Simultaneous localization and mapping Scalability Network architecture Cameras

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Reciprocal Transformations for Unsupervised Video Object Segmentation

Reciprocal Transformations for Unsupervised Video Object Seg...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ren, Sucheng Liu, Wenxi Liu, Yongtuo Chen, Haoxin Han, Guoqiang He, Shengfeng South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China

ISBN: (纸本)9781665445092

Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects as primary ones and rely on optical flow to capture the motion cues in videos, but the flow information alone is insufficient to distinguish the primary objects from the background objects that move together. This is because, when the noisy motion features are combined with the appearance features, the localization of the primary objects is misguided. To address this problem, we propose a novel reciprocal transformation network to discover primary objects by correlating three key factors: the intra-frame contrast, the motion cues, and temporal coherence of recurring objects. Each corresponds to a representative type of primary object, and our reciprocal mechanism enables an organic coordination of them to effectively remove ambiguous distractions from videos. Additionally, to exclude the information of the moving background objects from motion features, our transformation module enables to reciprocally transform the appearance features to enhance the motion features, so as to focus on the moving objects with salient appearance while removing the co-moving outliers. Experiments on the public benchmarks demonstrate that our model significantly outperforms the state-of-the-art methods.

关键词： Optical filters computer vision Transforms Object segmentation Coherence Search problems pattern recognition

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M³L: Language-based Video Editing via Multi-Modal Multi-Level Transformers

M<SUP>3</SUP>L: Language-based Video Editing via Multi-Modal...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Fu, Tsu-Jui Wang, Xin Eric Grafton, Scott T. Eckstein, Miguel P. Wang, William Yang UC Santa Barbara Santa Barbara CA 93106 USA UC Santa Cruz Santa Cruz CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Video editing tools are widely used nowadays for digital design. Although the demand for these tools is high, the prior knowledge required makes it difficult for novices to get started. Systems that could follow natural language instructions to perform automatic editing would significantly improve accessibility. This paper introduces the language-based video editing (LBVE) task, which allows the model to edit, guided by text instruction, a source video into a target video. LBVE contains two features: 1) the scenario of the source video is preserved instead of generating a completely different video;2) the semantic is presented differently in the target video, and all changes are controlled by the given instruction. We propose a Multi-Modal Multi-Level Transformer ((ML)-L-3) to carry out LBVE. (ML)-L-3 dynamically learns the correspondence between video perception and language semantic at different levels, which benefits both the video understanding and video frame synthesis. We build three new datasets for evaluation, including two diagnostic and one from natural videos with human-labeled text. Extensive experimental results show that (ML)-L-3 is effective for video editing and that LBVE can lead to a new field toward visionand-language research.

关键词： computer vision Fuses Semantics Natural languages Transformers pattern recognition Task analysis

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M³P: Learning Universal Representations via Multitask Multilingual Multimodal Pre-training

M<SUP>3</SUP>P: Learning Universal Representations via Multi...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ni, Minheng Huang, Haoyang Su, Lin Cui, Edward Bharti, Taroon Wang, Lijuan Zhang, Dongdong Duan, Nan Harbin Inst Technol Res Ctr Social Comp & Informat Retrieval Harbin Peoples R China Microsoft Res Asia Nat Language Comp Shanghai Peoples R China Microsoft Bing Multimedia Team Shanghai Peoples R China Microsoft Cloud AI Redmond WA USA

ISBN: (纸本)9781665445092

We present (MP)-P-3, a Multitask Multilingual Multimodal Pre-trained model that combines multilingual pre-training and multimodal pre-training into a unified framework via multitask pre-training. Our goal is to learn universal representations that can map objects occurred in different modalities or texts expressed in different languages into a common semantic space. In addition, to explicitly encourage fine-grained alignment between images and non-English languages, we also propose Multimodal Code-switched Training (MCT) to combine monolingual pre-training and multimodal pre-training via a code-switch strategy. Experiments are performed on the multilingual image retrieval task across two benchmark datasets, including MSCOCO and Multi3OK. (MP)-P-3 can achieve comparable results for English and new state-of-the-art results for non-English languages.

关键词： Training computer vision Computational modeling Semantics Image retrieval Benchmark testing Data models

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Coarse-to-Fine Feature Mining for Video Semantic Segmentation

Coarse-to-Fine Feature Mining for Video Semantic Segmentatio...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Sun, Guolei Liu, Yun Ding, Henghui Probst, Thomas Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Katholieke Univ Leuven VISICS Leuven Belgium

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The contextual information plays a core role in semantic segmentation. As for video semantic segmentation, the contexts include static contexts and motional contexts, corresponding to static content and moving content in a video clip, respectively. The static contexts are well exploited in image semantic segmentation by learning multiscale and global/long-range features. The motional contexts are studied in previous video semantic segmentation. However, there is no research about how to simultaneously learn static and motional contexts which are highly correlated and complementary to each other. To address this problem, we propose a Coarse-to-Fine Feature Mining (CFFM) technique to learn a unified presentation of static contexts and motional contexts. This technique consists of two parts: coarse-to-fine feature assembling and cross frame feature mining. The former operation prepares data for further processing, enabling the subsequent joint learning of static and motional contexts. The latter operation mines useful information/contexts from the sequential frames to enhance the video contexts of the features of the target frame. The enhanced features can be directly applied for the final prediction. Experimental results on popular benchmarks demonstrate that the proposed CFFM performs favorably against state-of-the-art methods for video semantic segmentation. Our implementation is available at https://***/GuoleiSun/VSS-CFFM.

关键词： Image segmentation computer vision Shape Motion segmentation Semantics Benchmark testing Data mining

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Dense specular shape from multiple specular flows

Dense specular shape from multiple specular flows

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IEEE conference on computer vision and pattern recognition

作者： Vasilyev, Yuriy Adato, Yair Zickler, Todd Ben-Shahar, Ohad Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA Ben Gurion Univ Negev Dept Comp Sci Beer Sheva Israel

ISBN: (纸本)9781424422425

The inference of specular (mirror-like) shape is a particularly difficult problem because an image of a specular object is nothing but a distortion of the surrounding environment. Consequently, when the environment is unknown, such an image would seem to convey little information about the shape itself It has recently been suggested (Adato et al., ICCV 2007) that observations of relative motion between a specular object and its environment can dramatically simplify the inference problem and allow one to recover shape without explicit knowledge of the environment content. However, this approach requires solving a non-linear PDE (the 'shape from specular flow equation') and analytic solutions are only known to exist for very constrained motions. In this paper, we consider the recovery of shape from specular flow under general motions. We show that while the 'shape from specular flow' PDE for a single motion is non-linear, we can combine observations of multiple specular flows from distinct relative motions to yield a linear set of equations. We derive necessary conditions for this procedure, discuss several numerical issues with their solution, and validate our results quantitatively using image data.

关键词： computer vision

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Multi-Decoding Deraining Network and Quasi-Sparsity Based Training

Multi-Decoding Deraining Network and Quasi-Sparsity Based Tr...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Wang, Yinglong Ma, Chao Zeng, Bing Univ Elect Sci & Technol China Chengdu Peoples R China Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781665445092

Existing deep deraining models are mainly learned via directly minimizing the statistical differences between rainy images and rain-free ground truths. They emphasize learning a mapping from rainy images to rain-free images with supervision. Despite the demonstrated success, these methods do not perform well on restoring the fine-grained local details or removing blurry rainy traces. In this work, we aim to exploit the intrinsic priors of rainy images and develop intrinsic loss functions to facilitate training deraining networks, which decompose a rainy image into a rainfree background layer and a rainy layer containing intact rain streaks. To this end, we introduce the quasi-sparsity prior to train network so as to generate two sparse layers with intact textures of different objects. Then we explore the low-value prior to compensate sparsity, forcing all rain streaks to enter into one layer while non-rain contents into another layer to restore image details. We introduce a multi-decoding structure to specially supervise the generation of multi-type deraining features. This helps to learn the most contributory features to deraining in respective spaces. Moreover, our model stabilizes the feature values from multi-spaces via information sharing to alleviate potential artifacts, which also accelerates the running speed. Extensive experiments show that the proposed deraining method outperforms the state-of-the-art approaches in terms of effectiveness and efficiency.

关键词： Training computer vision Rain Information sharing Image decomposition Image restoration pattern recognition

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