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

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Towards domain Generalization for multi-view 3d Object detection in Bird-Eye-view

Towards Domain Generalization for Multi-view 3D Object Detec...

引用

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

作者： Wang, Shuo Zhao, Xinhai Xu, Hai-Ming Chen, Zehui Yu, dameng Chang, Jiahao Yang, Zhen Zhao, Feng Univ Sci & Technol China Hefei Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9798350301298

multi-view 3d object detection (MV3d-det) in Bird-Eye-view (BEV) has drawn extensive attention due to its low cost and high efficiency. Although new algorithms for camera-only 3d object detection have been continuously proposed, most of them may risk drastic performance degradation when the domain of input images differs from that of training. In this paper, we first analyze the causes of the domain gap for the MV3d-det task. Based on the covariate shift assumption, we find that the gap mainly attributes to the feature distribution of BEV, which is determined by the quality of both depth estimation and 2d image's feature representation. To acquire a robust depth prediction, we propose to decouple the depth estimation from the intrinsic parameters of the camera (i.e. the focal length) through converting the prediction of metric depth to that of scale-invariant depth and perform dynamic perspective augmentation to increase the diversity of the extrinsic parameters (i.e. the camera poses) by utilizing homography. Moreover, we modify the focal length values to create multiple pseudo-domains and construct an adversarial training loss to encourage the feature representation to be more domain-agnostic. Without bells and whistles, our approach, namely dG-BEV, successfully alleviates the performance drop on the unseen target domain without impairing the accuracy of the source domain. Extensive experiments on Waymo, nuScenes, and Lyft, demonstrate the generalization and effectiveness of our approach.

关键词： 3d from multi-view and sensors

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Fast Monocular Scene Reconstruction with Global-Sparse Local-dense Grids

Fast Monocular Scene Reconstruction with Global-Sparse Local...

引用

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

作者： deng, Wei Choy, Chris Loop, Charles Litany, Or Zhu, Yuke Anandkurnar, Anima CMU RI Pittsburgh PA 15213 USA NVIDIA Res Stanford CA USA

ISBN: (纸本)9798350301298

Indoor scene reconstruction from monocular images has long been sought after by augmented reality and robotics developers. Recent advances in neural field representations and monocular priors have led to remarkable results in scene-level surface reconstructions. The reliance on multilayer Perceptrons (MLP), however, significantly limits speed in training and rendering. In this work, we propose to directly use signed distance function (SdF) in sparse voxel block grids for fast and accurate scene reconstruction without MLPs. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data such as color and semantic labels. To apply this representation to monocular scene reconstruction, we develop a scale calibration algorithm for fast geometric initialization from monocular depth priors. We apply differentiable volume rendering from this initialization to refine details with fast convergence. We also introduce efficient high-dimensional Continuous Random Fields (CRFs) to further exploit the semantic-geometry consistency between scene objects. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.

关键词： 3d from multi-view and sensors

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Progressive Spatio-temporal Alignment for Efficient Event-based Motion Estimation

Progressive Spatio-temporal Alignment for Efficient Event-ba...

引用

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

作者： Huang, Xueyan Zhang, Yueyi Xiong, Zhiwei Univ Sci & Technol China Langfang Peoples R China

ISBN: (纸本)9798350301298

In this paper, we propose an efficient event-based motion estimation framework for various motion models. different from previous works, we design a progressive event-to-map alignment scheme and utilize the spatio-temporal correlations to align events. In detail, we progressively align sampled events in an event batch to the time-surface map and obtain the updated motion model by minimizing a novel time-surface loss. In addition, a dynamic batch size strategy is applied to adaptively adjust the batch size so that all events in the batch are consistent with the current motion model. Our framework has three advantages: a) the progressive scheme refines motion parameters iteratively, achieving accurate motion estimation;b) within one iteration, only a small portion of events are involved in optimization, which greatly reduces the total runtime;c) the dynamic batch size strategy ensures that the constant velocity assumption always holds. We conduct comprehensive experiments to evaluate our framework on challenging high-speed scenes with three motion models: rotational, homography, and 6-dOF models. Experimental results demonstrate that our framework achieves state-of-the-art estimation accuracy and efficiency. The code is available at https://***/huangxueyan/PEME.

关键词： 3d from multi-view and sensors

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Cross-Guided Optimization of Radiance Fields with multi-view Image Super-Resolution for High-Resolution Novel view Synthesis

Cross-Guided Optimization of Radiance Fields with Multi-View...

引用

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

作者： Yoon, Youngho Yoon, Kuk-Jin Korea Adv Inst Sci & Technol Visual Intelligence Lab Daejeon South Korea

ISBN: (纸本)9798350301298

Novel view Synthesis (NVS) aims at synthesizing an image from an arbitrary viewpoint using multi-view images and camera poses. Among the methods for NVS, Neural Radiance Fields (NeRF) is capable of NVS for an arbitrary resolution as it learns a continuous volumetric representation. However, radiance fields rely heavily on the spectral characteristics of coordinate-based networks. Thus, there is a limit to improving the performance of high-resolution novel view synthesis (HRNVS). To solve this problem, we propose a novel framework using cross-guided optimization of the single-image super-resolution (SISR) and radiance fields. We perform multi-view image super-resolution (MVSR) on train-view images during the radiance fields optimization process. It derives the updated SR result by fusing the feature map obtained from SISR and voxel-based uncertainty fields generated by integrated errors of train-view images. By repeating the updates during radiance fields optimization, train-view images for radiance fields optimization have multi-view consistency and high-frequency details simultaneously, ultimately improving the performance of HRNVS. Experiments of HRNVS and MVSR on various benchmark datasets show that the proposed method significantly surpasses existing methods.

关键词： 3d from multi-view and sensors

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Adaptive Assignment for Geometry Aware Local Feature Matching

Adaptive Assignment for Geometry Aware Local Feature Matchin...

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

作者： Huang, dihe Chen, Ying Liu, Yong Liu, Jianlin Xu, Shang Wu, Wenlong ding, Yikang Tang, Fan Wang, Chengjie Tsinghua Univ Beijing Peoples R China Tencent YouTu Lab Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9798350301298

The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale and viewpoint variations, due to the geometric inconsistency resulting from the application of the mutual nearest neighbour criterion (i.e., one-to-one assignment) in patch-level matching. Accordingly, we introduce AdaMatcher, which first accomplishes the feature correlation and co-visible area estimation through an elaborate feature interaction module, then performs adaptive assignment on patch-level matching while estimating the scales between images, and finally refines the co-visible matches through scale alignment and sub-pixel regression module. Extensive experiments show that AdaMatcher outperforms solid baselines and achieves state-of-the-art results on many downstream tasks. Additionally, the adaptive assignment and sub-pixel refinement module can be used as a refinement network for other matching methods, such as SuperGlue, to boost their performance further. The code will be publicly available at https://***/AbyssGaze/AdaMatcher.

关键词： 3d from multi-view and sensors

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Neuralangelo: High-Fidelity Neural Surface Reconstruction

Neuralangelo: High-Fidelity Neural Surface Reconstruction

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

作者： Li, Zhaoshuo Muller, Thomas Evans, Alex Tayloi, Russell H. Unberath, Mathias Liu, Ming -Yu Lin, Chen-Hsuan NVIDIA Res Santa Clara CA 95051 USA Johns Hopkins Univ Baltimore MD 21218 USA

ISBN: (纸本)9798350301298

Neural surface reconstruction has been shown to be powerful for recovering dense 3d surfaces via image-based neural rendering. However, current methods struggle to recover detailed structures of real-world scenes. To address the issue, we present Neuralangelo, which combines the representation power of multi-resolution 3d hash grids with neural surface rendering. Two key ingredients enable our approach: (1) numerical gradients for computing higher-order derivatives as a smoothing operation and (2) coarse-to-fine optimization on the hash grids controlling different levels of details. Even without auxiliary inputs such as depth, Neuralangelo can effectively recover dense 3d surface structures from multi-view images with fidelity significantly surpassing previous methods, enabling detailed large-scale scene reconstruction from RGB video captures.

关键词： 3d from multi-view and sensors

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Instant multi-view Head Capture through Learnable Registration

Instant Multi-View Head Capture through Learnable Registrati...

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

作者： Bolkart, Timo Li, Tianye Black, Michael J. MPI Intelligent Syst Tubingen Germany Univ Southern Calif Los Angeles CA USA

ISBN: (纸本)9798350301298

Existing methods for capturing datasets of 3d heads in dense semantic correspondence are slow and commonly address the problem in two separate steps;multi-view stereo (MVS) reconstruction followed by non-rigid registration. To simplify this process, we introduce TEMPEH (Towards Estimation of 3d Meshes from Performances of Expressive Heads) to directly infer 3d heads in dense correspondence from calibrated multi-view images. Registering datasets of 3d scans typically requires manual parameter tuning to find the right balance between accurately fitting the scans' surfaces and being robust to scanning noise and outliers. Instead, we propose to jointly register a 3d head dataset while training TEMPEH. Specifically, during training, we minimize a geometric loss commonly used for surface registration, effectively leveraging TEMPEH as a regularizer. Our multi-view head inference builds on a volumetric feature representation that samples and fuses features from each view using camera calibration information. To account for partial occlusions and a large capture volume that enables head movements, we use view- and surface-aware feature fusion, and a spatial transformer-based head localization module, respectively. We use raw MVS scans as supervision during training, but, once trained, TEMPEH directly predicts 3d heads in dense correspondence without requiring median reconstruction error of 0.26 mm, 64% lower than the current state-of-the-art. This enables the efficient capture of large datasets containing multiple people and diverse facial motions.

关键词： 3d from multi-view and sensors

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Rotation-Invariant Transformer for Point Cloud Matching

Rotation-Invariant Transformer for Point Cloud Matching

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

作者： Yu, Hao Qin, Zheng Hou, Ji Saleh, Mahdi Li, dongsheng Busam, Benjamin Ilic, Slobodan Tech Univ Munich Munich Germany NUDT Changsha Peoples R China Meta Real Labs Menlo Pk CA USA 3Dwe Ai Munich Germany Siemens AG Munich Germany

ISBN: (纸本)9798350301298

The intrinsic rotation invariance lies at the core of matching point clouds with handcrafted descriptors. However, it is widely despised by recent deep matchers that obtain the rotation invariance extrinsically via data augmentation. As the finite number of augmented rotations can never span the continuous SO(3) space, these methods usually show instability when facing rotations that are rarely seen. To this end, we introduce RoITr, a Rotation-Invariant Transformer to cope with the pose variations in the point cloud matching task. We contribute both on the local and global levels. Starting from the local level, we introduce an attention mechanism embedded with Point Pair Feature (PPF)-based coordinates to describe the pose-invariant geometry, upon which a novel attention-based encoder-decoder architecture is constructed. We further propose a global transformer with rotation-invariant cross-frame spatial awareness learned by the self-attention mechanism, which significantly improves the feature distinctiveness and makes the model robust with respect to the low overlap. Experiments are conducted on both the rigid and non-rigid public benchmarks, where RoITr outperforms all the state-of-the-art models by a considerable margin in the low-overlapping scenarios. Especially when the rotations are enlarged on the challenging 3dLoMatch benchmark, RoITr surpasses the existing methods by at least 13 and 5 percentage points in terms of Inlier Ratio and Registration Recall, respectively. Code is publicly available (1).

关键词： 3d from multi-view and sensors

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Temporal Interpolation Is All You Need for dynamic Neural Radiance Fields

Temporal Interpolation Is All You Need for Dynamic Neural Ra...

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

作者： Park, Sungheon Son, Minjung Tang, Seokhwan Ahn, Young Chun Kim, Ji-Yeon Kang, Nahyup Samsung Adv Inst Technol SAIT Suwon South Korea

ISBN: (纸本)9798350301298

Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feature vectors. Two feature interpolation methods are suggested depending on underlying representations, neural networks or grids. In the neural representation, we extract features from space-time inputs via multiple neural network modules and interpolate them based on time frames. The proposed multi-level feature interpolation network effectively captures features of both short-term and long-term time ranges. In the grid representation, space-time features are learned via four-dimensional hash grids, which remarkably reduces training time. The grid representation shows more than 100x faster training speed than the previous neural-net-based methods while maintaining the rendering quality. Concatenating static and dynamic features and adding a simple smoothness term further improve the performance of our proposed models. despite the simplicity of the model architectures, our method achieved state-of-the-art performance both in rendering quality for the neural representation and in training speed for the grid representation.

关键词： 3d from multi-view and sensors

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GeoMVSNet: Learning multi-view Stereo with Geometry Perception

GeoMVSNet: Learning Multi-View Stereo with Geometry Percepti...

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

作者： Zhang, Zhe Peng, Rui Hu, Yuxi Wang, Ronggang Peking Univ Sch Elect & Comp Engn Beijing Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Recent cascade multi-view Stereo (MVS) methods can efficiently estimate high-resolution depth maps through narrowing hypothesis ranges. However, previous methods ignored the vital geometric information embedded in coarse stages, leading to vulnerable cost matching and sub-optimal reconstruction results. In this paper, we propose a geometry awareness model, termed GeoMVSNet, to explicitly integrate geometric clues implied in coarse stages for delicate depth estimation. In particular, we design a two-branch geometry fusion network to extract geometric priors from coarse estimations to enhance structural feature extraction at finer stages. Besides, we embed the coarse probability volumes, which encode valuable depth distribution attributes, into the lightweight regularization network to further strengthen depth-wise geometry intuition. Meanwhile, we apply the frequency domain filtering to mitigate the negative impact of the high-frequency regions and adopt the curriculum learning strategy to progressively boost the geometry integration of the model. To intensify the full-scene geometry perception of our model, we present the depth distribution similarity loss based on the Gaussian-Mixture Model assumption. Extensive experiments on dTU and Tanks and Temples (T&T) datasets demonstrate that our GeoMVSNet achieves state-of-the-art results and ranks first on the T&T-Advanced set. Code is available at https://***/doubleZ0108/GeoMVSNet.

关键词： 3d from multi-view and sensors

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