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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1-10 订阅

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NEF: Neural Edge Fields for 3d Parametric Curve Reconstruction from multi-view Images

NEF: Neural Edge Fields for 3D Parametric Curve Reconstructi...

引用

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

作者： Ye, Yunfan Yi, Renjiao Gao, Zhirui Thu, Chenyang Cai, Zhiping Xu, Kai Natl Univ Def Technol Changsha Hunan Peoples R China

ISBN: (纸本)9798350301298

We study the problem of reconstructing 3d feature curves of an object from a set of calibrated multi-view images. To do so, we learn a neural implicit field representing the density distribution of 3d edges which we refer to as Neural Edge Field (NEF). Inspired by NeRF [20], NEF is optimized with a view-based rendering loss where a 2d edge map is rendered at a given view and is compared to the ground-truth edge map extracted from the image of that view. The rendering-based differentiable optimization of NEF fully exploits 2d edge detection, without needing a supervision of 3d edges, a 3d geometric operator or cross-view edge correspondence. Several technical designs are devised to ensure learning a range-limited and view-independent NEF for robust edge extraction. The final parametric 3d curves are extracted from NEF with an iterative optimization method. On our benchmark with synthetic data, we demonstrate that NEF outperforms existing state-of-the-art methods on all metrics. Project page: https://***/NEF/.

关键词： 3d from multi-view and sensors

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NeRF-HuGS: Improved Neural Radiance Fields in Non-static Scenes Using Heuristics-Guided Segmentation

NeRF-HuGS: Improved Neural Radiance Fields in Non-static Sce...

引用

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

作者： Chen, Jiahao Qin, Yipeng Liu, Lingjie Lu, Jiangbo Li, Guanbin Sun Yat Sen Univ Guangzhou Peoples R China Cardiff Univ Cardiff Wales Univ Penn Philadelphia PA USA SmartMore Corp Hong Kong Peoples R China

ISBN: (纸本)9798350353006

Neural Radiance Field (NeRF) has been widely recognized for its excellence in novel view synthesis and 3d scene reconstruction. However, their effectiveness is inherently tied to the assumption of static scenes, rendering them susceptible to undesirable artifacts when confronted with transient distractors such as moving objects or shadows. In this work, we propose a novel paradigm, namely "Heuristics-Guided Segmentation" (HuGS), which significantly enhances the separation of static scenes from transient distractors by harmoniously combining the strengths of hand-crafted heuristics and state-of-the-art segmentation models, thus significantly transcending the limitations of previous solutions. Furthermore, we delve into the meticulous design of heuristics, introducing a seamless fusion of Structure-from-Motion (SfM)-based heuristics and color residual heuristics, catering to a diverse range of texture profiles. Extensive experiments demonstrate the superiority and robustness of our method in mitigating transient distractors for NeRFs trained in non-static scenes. Project page: https://***/NeRF-HuGS/

关键词： 3d from multi-view and sensors

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FlowIBR: Leveraging Pre-Training for Efficient Neural Image-Based Rendering of dynamic Scenes

FlowIBR: Leveraging Pre-Training for Efficient Neural Image-...

引用

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

作者： Busching, Marcel Bengtson, Josef Nilsson, david Bjorkman, Marten KTH Royal Insitute Technol Stockholm Sweden Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9798350365474

We introduce FlowIBR, a novel approach for efficient monocular novel view synthesis of dynamic scenes. Existing techniques already show impressive rendering quality but tend to focus on optimization within a single scene without leveraging prior knowledge, resulting in long optimization times per scene. FlowIBR circumvents this limitation by integrating a neural image-based rendering method, pre-trained on a large corpus of widely available static scenes, with a per-scene optimized scene flow field. Utilizing this flow field, we bend the camera rays to counteract the scene dynamics, thereby presenting the dynamic scene as if it were static to the rendering network. The proposed method reduces per-scene optimization time by an order of magnitude, achieving comparable rendering quality to existing methods - all on a single consumer-grade GPU.

关键词： 3d from multi-view and sensors dynamic scenes Neural rendering

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HiFi4G: High-Fidelity Human Performance Rendering via Compact Gaussian Splatting

HiFi4G: High-Fidelity Human Performance Rendering via Compac...

引用

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

作者： Jiang, Yuheng Shen, Zhehao Wang, Penghao Su, Zhuo Hong, Yu Zhang, Yingliang Yu, Jingyi Xu, Lan ShanghaiTech Univ Shanghai Peoples R China NeuDim Shanghai Peoples R China ByteDance Beijing Peoples R China DGene Baton Rouge LA USA

ISBN: (纸本)9798350353006

We have recently seen tremendous progress in photo-real human modeling and rendering. Yet, efficiently rendering realistic human performance and integrating it into the rasterization pipeline remains challenging. In this paper, we present HiFi4G, an explicit and compact Gaussian-based approach for high-fidelity human performance rendering from dense footage. Our core intuition is to marry the 3d Gaussian representation with non-rigid tracking, achieving a compact and compression-friendly representation. We first propose a dual-graph mechanism to obtain motion priors, with a coarse deformation graph for effective initialization and a fine-grained Gaussian graph to enforce subsequent constraints. Then, we utilize a 4d Gaussian optimization scheme with adaptive spatial-temporal regularizers to effectively balance the non-rigid prior and Gaussian updating. We also present a companion compression scheme with residual compensation for immersive experiences on various platforms. It achieves a substantial compression rate of approximately 25 times, with less than 2MB of storage per frame. Extensive experiments demonstrate the effectiveness of our approach, which significantly outperforms existing approaches in terms of optimization speed, rendering quality, and storage overhead. Project page: https://***/HiFi4G/.

关键词： 3d from multi-view and sensors Compact Gaussian Splatting Human Performance Capture Volume Rendering

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Masked Spatial Propagation Network for Sparsity-Adaptive depth Refinement

Masked Spatial Propagation Network for Sparsity-Adaptive Dep...

引用

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

作者： Jun, Jinyoung Lee, Jae-Han Kim, Chang-Su Korea Univ Seoul South Korea Gauss Labs Inc Palo Alto CA USA

ISBN: (纸本)9798350353006

The main function of depth completion is to compensate for an insufficient and unpredictable number of sparse depth measurements of hardware sensors. However, existing research on depth completion assumes that the sparsity - the number of points or LidAR lines - is fixed for training and testing. Hence, the completion performance drops severely when the number of sparse depths changes significantly. To address this issue, we propose the sparsity-adaptive depth refinement (SdR) framework, which refines monocular depth estimates using sparse depth points. For SdR, we propose the masked spatial propagation network (MSPN) to perform SdR with a varying number of sparse depths effectively by gradually propagating sparse depth information throughout the entire depth map. Experimental results demonstrate that MPSN achieves state-of-the-art performance on both SdR and conventional depth completion scenarios. Codes are available at https://***/jyjunmcl/MSPN_SdR

关键词： 3d from multi-view and sensors depth completion depth refinement

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Cam4dOcc: Benchmark for Camera-Only 4d Occupancy Forecasting in Autonomous driving Applications

Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting...

引用

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

作者： Ma, Junyi Chen, Xieyuanli Huang, Jiawei Xu, Jingyi Luo, Zhen Xu, Jintao Gu, Weihao Ai, Rui Wangi, Hesheng Shanghai Jiao Tong Univ Dept Automat lRMV Lab Shanghai Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha Peoples R China HAOMO AI Beijing Peoples R China Shanghai Jiao Tong Univ NAV Shanghai Peoples R China Beijing Inst Technol IVRC Beijing Peoples R China

ISBN: (纸本)9798350353006

Understanding how the surrounding environment changes is crucial for performing downstream tasks safely and reliably in autonomous driving applications. Recent occupancy estimation techniques using only camera images as input can provide dense occupancy representations of large-scale scenes based on the current observation. However, they are mostly limited to representing the current 3d space and do not consider the future state of surrounding objects along the time axis. To extend camera- only occupancy estimation into spatiotemporal prediction, we propose Cam4dOcc, a new benchmark for camera-only 4d occupancy forecasting, evaluating the surrounding scene changes in a near future. We build our benchmark based on multiple publicly available datasets, including nuScenes, nuScenes-Occupancy, and Lyft-Level5, which provides sequential occupancy states of general movable and static objects, as well as their 3d backward centripetal flow. To establish this benchmark for future research with comprehensive comparisons, we introduce four baseline types from diverse camera-based perception and prediction implementations, including a static-world occupancy model, voxelization of point cloud prediction, 2d-3d instance-based prediction, and our proposed novel end-to-end 4d occupancy forecasting network. Furthermore, the standardized evaluation protocol for preset multiple tasks is also provided to compare the performance of all the proposed baselines on present and future occupancy estimation with respect to objects of interest in autonomous driving scenarios. The dataset and our implementation of all four baselines in the proposed Cam4dOcc benchmark are released as open source at https://***/haomo-ai/Cam4dOcc.

关键词： 3d from multi-view and sensors occupancy forecasting occupancy prediction

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SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3d Mesh Reconstruction and High-Quality Mesh Rendering

SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D M...

引用

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

作者： Guedon, Antoine Lepetit, Vincent Univ Gustave Eiffel CNRS LIGM Ecole Ponts Champs Sur Marne France

ISBN: (纸本)9798350353013;9798350353006

We propose a method to allow precise and extremely fast mesh extraction from 3d Gaussian Splatting [15]. Gaussian Splatting has recently become very popular as it yields realistic rendering while being significantly faster to train than NeRFs. It is however challenging to extract a mesh from the millions of tiny 3d Gaussians as these Gaussians tend to be unorganized after optimization and no method has been proposed so far. Our first key contribution is a regularization term that encourages the Gaussians to align well with the surface of the scene. We then introduce a method that exploits this alignment to extract a mesh from the Gaussians using Poisson reconstruction, which is fast, scalable, and preserves details, in contrast to the Marching Cubes algorithm usually applied to extract meshes from Neural SdFs. Finally, we introduce an optional refinement strategy that binds Gaussians to the surface of the mesh, and jointly optimizes these Gaussians and the mesh through Gaussian splatting rendering. This enables easy editing, sculpting, animating, and relighting of the Gaussians by manipulating the mesh instead of the Gaussians themselves. Retrieving such an editable mesh for realistic rendering is done within minutes with our method, compared to hours with the state-of-the-art method on SdFs, while providing a better rendering quality.

关键词： 3d from multi-view and sensors differentiable Rendering Gaussian Splatting Mesh Novel view Synthesis Surface reconstruction

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NeRFVS: Neural Radiance Fields for Free view Synthesis via Geometry Scaffolds

NeRFVS: Neural Radiance Fields for Free View Synthesis via G...

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

作者： Yang, Chen Li, Peihao Zhou, Zanwei Yuan, Shanxin Liu, Bingbing Yang, Xiaokang Qiu, Weichao Shen, Wei Shanghai Jiao Tong Univ AI Inst MoE Key Lab Artificial Intelligence Shanghai Peoples R China Huawei Noahs Ark Lab Montreal PQ Canada Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798350301298

We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views. To address this issue, we utilize the holistic priors, including pseudo depth maps and view coverage information, from neural reconstruction to guide the learning of implicit neural representations of 3d indoor scenes. Concretely, an off-the-shelf neural reconstruction method is leveraged to generate a geometry scaffold. Then, two loss functions based on the holistic priors are proposed to improve the learning of NeRF: 1) A robust depth loss that can tolerate the error of the pseudo depth map to guide the geometry learning of NeRF;2) A variance loss to regularize the variance of implicit neural representations to reduce the geometry and color ambiguity in the learning procedure. These two loss functions are modulated during NeRF optimization according to the view coverage information to reduce the negative influence brought by the view coverage imbalance. Extensive results demonstrate that our NeRFVS outperforms state-of-the-art view synthesis methods quantitatively and qualitatively on indoor scenes, achieving high-fidelity free navigation results.

关键词： 3d from multi-view and sensors

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Finding Geometric Models by Clustering in the Consensus Space

Finding Geometric Models by Clustering in the Consensus Spac...

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

作者： Barath, daniel Rozumnyi, denys Eichhardt, Ivan Hajder, Levente Matas, Jiri Swiss Fed Inst Technol Comp Vis & Geometry Grp Zurich Switzerland CTU Fac Elect Engn VRG Prague Czech Republic TMEIC Corp Americas Roanoke VA USA Eotvos Lorand Univ Budapest Hungary

ISBN: (纸本)9798350301298

We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies;trajectory estimation of fast-moving objects;and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://***/danini/clustering-in-consensus-space.

关键词： 3d from multi-view and sensors

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Learnable Skeleton-Aware 3d Point Cloud Sampling

Learnable Skeleton-Aware 3D Point Cloud Sampling

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

作者： Wen, Cheng Yu, Baosheng Tao, dacheng Univ Sydney Sch Comp Sci Sydney NSW 2008 Australia

ISBN: (纸本)9798350301298

Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-to-sample methods have recently received increasing attention from the community for jointly training with downstream tasks. However, the above-mentioned task-specific sampling methods usually fail to explore the geometries of objects in an explicit manner. In this paper, we introduce a new skeleton-aware learning-to-sample method by learning object skeletons as the prior knowledge to preserve the object geometry and topology information during sampling. Specifically, without labor-intensive annotations per object category, we first learn category-agnostic object skeletons via the medial axis transform definition in an unsupervised manner. With object skeleton, we then evaluate the histogram of the local feature size as the prior knowledge to formulate skeleton-aware sampling from a probabilistic perspective. Additionally, the proposed skeleton-aware sampling pipeline with the task network is thus end-to-end trainable by exploring the reparameterization trick. Extensive experiments on three popular downstream tasks, point cloud classification, retrieval, and reconstruction, demonstrate the effectiveness of the proposed method for efficient point cloud analysis.

关键词： 3d from multi-view and sensors

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