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

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FeatureBooster: Boosting Feature descriptors with a Lightweight Neural Network

FeatureBooster: Boosting Feature Descriptors with a Lightwei...

引用

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

作者： Wang, Xinjiang Liu, Zeyu Hu, Yu Xi, Wei Yu, Wenxian Zou, danping Shanghai Jiao Tong Univ Shanghai Key Lab Nav & Locat Based Serv Shanghai Peoples R China SJTU SEIEE G60 Yun Zhi AI Innovat & Applicat Res Shanghai Peoples R China Midea Corp Res Ctr Intelligent Percept Inst Louisville KY USA

ISBN: (纸本)9798350301298

We introduce a lightweight network to improve descriptors of keypoints within the same image. The network takes the original descriptors and the geometric properties of keypoints as the input, and uses an MLP-based self-boosting stage and a Transformer-based cross-boosting stage to enhance the descriptors. The boosted descriptors can be either real-valued or binary ones. We use the proposed network to boost both hand-crafted (ORB [34], SIFT [24]) and the state-of-the-art learning-based descriptors (SuperPoint [10], ALIKE [53]) and evaluate them on image matching, visual localization, and structure-from-motion tasks. The results show that our method significantly improves the performance of each task, particularly in challenging cases such as large illumination changes or repetitive patterns. Our method requires only 3.2ms on desktop GPU and 27ms on embedded GPU to process 2000 features, which is fast enough to be applied to a practical system. The code and trained weights are publicly available at ***/SJTU-ViSYS/FeatureBooster.

关键词： 3d from multi-view and sensors

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EditableNeRF: Editing Topologically Varying Neural Radiance Fields by Key Points

EditableNeRF: Editing Topologically Varying Neural Radiance ...

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

作者： Zheng, Chengwei Lin, Wenbin Xu, Feng Tsinghua Univ Sch Software Beijing Peoples R China Tsinghua Univ BNRist Beijing Peoples R China

ISBN: (纸本)9798350301298

Neural radiance fields (NeRF) achieve highly photo-realistic novel-view synthesis, but it's a challenging problem to edit the scenes modeled by NeRF-based methods, especially for dynamic scenes. We propose editable neural radiance fields that enable end-users to easily edit dynamic scenes and even support topological changes. Input with an image sequence from a single camera, our network is trained fully automatically and models topologically varying dynamics using our picked-out surface key points. Then end-users can edit the scene by easily dragging the key points to desired new positions. To achieve this, we propose a scene analysis method to detect and initialize key points by considering the dynamics in the scene, and a weighted key points strategy to model topologically varying dynamics by joint key points and weights optimization. Our method supports intuitive multi-dimensional (up to 3d) editing and can generate novel scenes that are unseen in the input sequence. Experiments demonstrate that our method achieves high-quality editing on various dynamic scenes and outperforms the state-of-the-art. Our code and captured data are available at https://***/EditableNeRF/.

关键词： 3d from multi-view and sensors

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Learning Neural Volumetric Representations of dynamic Humans in Minutes

Learning Neural Volumetric Representations of Dynamic Humans...

引用

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

作者： Geng, Chen Peng, Sida Xu, Zhen Bao, Hujun Zhou, Xiaowei Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China

ISBN: (纸本)9798350301298

This paper addresses the challenge of efficiently reconstructing volumetric videos of dynamic humans from sparse multi-view videos. Some recent works represent a dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from input videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets to reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric representations of dynamic humans in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose a novel 2d motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than previous per-scene optimization methods while being competitive in the rendering quality. Training our model on a 512 x 512 video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code is available on our project page: https://***/instant nvr.

关键词： 3d from multi-view and sensors

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SE-ORNet: Self-Ensembling Orientation-aware Network for Unsupervised Point Cloud Shape Correspondence

SE-ORNet: Self-Ensembling Orientation-aware Network for Unsu...

引用

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

作者： deng, Jiacheng Wang, Chuxin Lu, Jiahao He, Jianfeng Zhang, Tianzhu Yu, Jiyang Zhang, Zhe Univ Sci & Technol China Hefei Peoples R China China Acad Space Technol Beijing Peoples R China Deep Space Explorat Lab Nanjing Peoples R China

ISBN: (纸本)9798350301298

Unsupervised point cloud shape correspondence aims to obtain dense point-to-point correspondences between point clouds without manually annotated pairs. However, humans and some animals have bilateral symmetry and various orientations, which lead to severe mispredictions of symmetrical parts. Besides, point cloud noise disrupts consistent representations for point cloud and thus degrades the shape correspondence accuracy. To address the above issues, we propose a Self-Ensembling ORientation-aware Network termed SE-ORNet. The key of our approach is to exploit an orientation estimation module with a domain adaptive discriminator to align the orientations of point cloud pairs, which significantly alleviates the mispredictions of symmetrical parts. Additionally, we design a self-ensembling framework for unsupervised point cloud shape correspondence. In this framework, the disturbances of point cloud noise are overcome by perturbing the inputs of the student and teacher networks with different data augmentations and constraining the consistency of predictions. Extensive experiments on both human and animal datasets show that our SE-ORNet can surpass state-of-the-art unsupervised point cloud shape correspondence methods.

关键词： 3d from multi-view and sensors

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Collaboration Helps Camera Overtake LidAR in 3d detection

Collaboration Helps Camera Overtake LiDAR in 3D Detection

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

作者： Hu, Yue Lu, Yifan Xu, Runsheng Xie, Weidi Chen, Siheng Wang, Yanfeng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai 200030 Peoples R China Univ Calif Los Angeles Los Angeles CA 90024 USA Shanghai AI Lab Shanghai Peoples R China

ISBN: (纸本)9798350301298

Camera-only 3d detection provides an economical solution with a simple configuration for localizing objects in 3d space compared to LidAR-based detection systems. However, a major challenge lies in precise depth estimation due to the lack of direct 3d measurements in the input. Many previous methods attempt to improve depth estimation through network designs, e.g., deformable layers and larger receptive fields. This work proposes an orthogonal direction, improving the camera-only 3d detection by introducing multi-agent collaborations. Our proposed collaborative camera-only 3d detection (CoCa3d) enables agents to share complementary information with each other through communication. Meanwhile, we optimize communication efficiency by selecting the most informative cues. The shared messages from multiple viewpoints disambiguate the single-agent estimated depth and complement the occluded and long-range regions in the single-agent view. We evaluate CoCa3d in one real-world dataset and two new simulation datasets. Results show that CoCa3d improves previous SOTA performances by 44.21% on dAIR-V2X, 30.60% on OPV2V+, 12.59% on CoPerception-UAVs+ for AP@70. Our preliminary results show a potential that with sufficient collaboration, the camera might overtake LidAR in some practical scenarios. We released the dataset and code.

关键词： 3d from multi-view and sensors

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ESLAM: Efficient dense SLAM System Based on Hybrid Representation of Signed distance Fields

ESLAM: Efficient Dense SLAM System Based on Hybrid Represent...

引用

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

作者： Johari, Mohammad Mahdi Carta, Camilla Fleuret, Francois Ecole Polytech Fed Lausanne Idiap Res Inst Lausanne Switzerland Ams OSRAM Martigny Switzerland Univ Geneva EPFL Geneva Switzerland

ISBN: (纸本)9798350301298

We present ESLAM, an efficient implicit neural representation method for Simultaneous Localization and Mapping (SLAM). ESLAM reads RGB-d frames with unknown camera poses in a sequential manner and incrementally reconstructs the scene representation while estimating the current camera position in the scene. We incorporate the latest advances in Neural Radiance Fields (NeRF) into a SLAM system, resulting in an efficient and accurate dense visual SLAM method. Our scene representation consists of multiscale axis-aligned perpendicular feature planes and shallow decoders that, for each point in the continuous space, decode the interpolated features into Truncated Signed distance Field (TSdF) and RGB values. Our extensive experiments on three standard datasets, Replica, ScanNet, and TUM RGB-d show that ESLAM improves the accuracy of 3d reconstruction and camera localization of state-of-the-art dense visual SLAM methods by more than 50%, while it runs up to x10 faster and does not require any pre-training. Project page: https://***/paper/eslam

关键词： 3d from multi-view and sensors

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SparsePose: Sparse-view Camera Pose Regression and Refinement

SparsePose: Sparse-View Camera Pose Regression and Refinemen...

引用

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

作者： Sinha, Samarth Zhang, Jason Y. Tagliasacchi, Andrea Gilitschenski, Igor Lindell, david B. Univ Toronto Toronto ON Canada Carnegie Mellon Univ Pittsburgh PA USA Simon Fraser Univ Burnaby BC Canada Google Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9798350301298

Camera pose estimation is a key step in standard 3d reconstruction pipelines that operate on a dense set of images of a single object or scene. However, methods for pose estimation often fail when only a few images are available because they rely on the ability to robustly identify and match visual features between image pairs. While these methods can work robustly with dense camera views, capturing a large set of images can be time-consuming or impractical. We propose SparsePose for recovering accurate camera poses given a sparse set of wide-baseline images (fewer than 10). The method learns to regress initial camera poses and then iteratively refine them after training on a large-scale dataset of objects (Co3d: Common Objects in 3d). SparsePose significantly outperforms conventional and learning-based baselines in recovering accurate camera rotations and translations. We also demonstrate our pipeline for high-fidelity 3d reconstruction using only 5-9 images of an object. Project webpage: https://***/

关键词： 3d from multi-view and sensors

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Seeing Through the Glass: Neural 3d Reconstruction of Object Inside a Transparent Container

Seeing Through the Glass: Neural 3D Reconstruction of Object...

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

作者： Tong, Jinguang Muthu, Sundaram Maken, Fahira Afzal Nguyen, Chuong Li, Hongdong Australian Natl Univ Canberra ACT Australia CSIRO Data61 Sydney NSW Australia

ISBN: (纸本)9798350301298

In this paper, we define a new problem of recovering the 3d geometry of an object confined in a transparent enclosure. We also propose a novel method for solving this challenging problem. Transparent enclosures pose challenges of multiple light reflections and refractions at the interface between different propagation media e.g. air or glass. These multiple reflections and refractions cause serious image distortions which invalidate the single viewpoint assumption. Hence the 3d geometry of such objects cannot be reliably reconstructed using existing methods, such as traditional structure from motion or modern neural reconstruction methods. We solve this problem by explicitly modeling the scene as two distinct sub-spaces, inside and outside the transparent enclosure. We use an existing neural reconstruction method (NeuS) that implicitly represents the geometry and appearance of the inner subspace. In order to account for complex light interactions, we develop a hybrid rendering strategy that combines volume rendering with ray tracing. We then recover the underlying geometry and appearance of the model by minimizing the difference between the real and rendered images. We evaluate our method on both synthetic and real data. Experiment results show that our method outperforms the state-of-the-art (SOTA) methods. Codes and data will be available at https://***/hirotong/ReNeuS

关键词： 3d from multi-view and sensors

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Robust Outlier Rejection for 3d Registration with Variational Bayes

Robust Outlier Rejection for 3D Registration with Variationa...

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

作者： Jiang, Haobo dang, Zheng Wei, Zhen Xie, Jin Yang, Jian Salzmann, Mathieu Nanjing Univ Sci & Technol PCA Lab Nanjing Peoples R China Ecole Polytech Fed Lausanne CVLab Lausanne Switzerland

ISBN: (纸本)9798350301298

Learning-based outlier (mismatched correspondence) rejection for robust 3d registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. In this paper, we develop a novel variational non-local network-based outlier rejection framework for robust alignment. By reformulating the non-local feature learning with variational Bayesian inference, the Bayesian-driven long-range dependencies can be modeled to aggregate discriminative geometric context information for inlier/outlier distinction. Specifically, to achieve such Bayesian-driven contextual dependencies, each query/key/value component in our nonlocal network predicts a prior feature distribution and a posterior one. Embedded with the inlier/outlier label, the posterior feature distribution is label-dependent and discriminative. Thus, pushing the prior to be close to the discriminative posterior in the training step enables the features sampled from this prior at test time to model high-quality long-range dependencies. Notably, to achieve effective posterior feature guidance, a specific probabilistic graphical model is designed over our non-local model, which lets us derive a variational low bound as our optimization objective for model training. Finally, we propose a voting-based inlier searching strategy to cluster the high-quality hypothetical inliers for transformation estimation. Extensive experiments on 3dMatch, 3dLoMatch, and KITTI datasets verify the effectiveness of our method. Code is available at https://***/Jiang-HB/VBReg.

关键词： 3d from multi-view and sensors

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Masked Wavelet Representation for Compact Neural Radiance Fields

Masked Wavelet Representation for Compact Neural Radiance Fi...

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

作者： Rho, daniel Lee, Byeonghyeon Nam, Seungtae Lee, Joo Chan Ko, Jong Hwan Park, Eunbyung AI2XL KT Seoul South Korea Sungkyunkwan Univ Dept Artificial Intelligence Seoul South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Seoul South Korea

ISBN: (纸本)9798350301298

Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. However, using a multi-layer perceptron (MLP) to represent a 3d scene or object requires enormous computational resources and time. There have been recent studies on how to reduce these computational inefficiencies by using additional data structures, such as grids or trees. despite the promising performance, the explicit data structure necessitates a substantial amount of memory. In this work, we present a method to reduce the size without compromising the advantages of having additional data structures. In detail, we propose using the wavelet transform on grid-based neural fields. Grid-based neural fields are for fast convergence, and the wavelet transform, whose efficiency has been demonstrated in high-performance standard codecs, is to improve the parameter efficiency of grids. Furthermore, in order to achieve a higher sparsity of grid coefficients while maintaining reconstruction quality, we present a novel trainable masking approach. Experimental results demonstrate that non-spatial grid coefficients, such as wavelet coefficients, are capable of attaining a higher level of sparsity than spatial grid coefficients, resulting in a more compact representation. With our proposed mask and compression pipeline, we achieved state-of-the-art performance within a memory budget of 2 MB. Our code is available at https://***/daniel03c1/masked wavelet nerf.

关键词： 3d from multi-view and sensors

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