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

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RGBd2: Generative Scene Synthesis via Incremental view Inpainting using RGBd diffusion Models

RGBD2: Generative Scene Synthesis via Incremental View Inpai...

引用

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

作者： Lei, Jiabao Tang, Jiapeng Jia, Kui South China Univ Technol Guangzhou Guangdong Peoples R China Tech Univ Munich Munich Germany Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

We address the challenge of recovering an underlying scene geometry and colors from a sparse set of RGBd view observations. In this work, we present a new solution termed RGBd2 that sequentially generates novel RGBd views along a camera trajectory, and the scene geometry is simply the fusion result of these views. More specifically, we maintain an intermediate surface mesh used for rendering new RGBd views, which subsequently becomes complete by an inpainting network;each rendered RGBd view is later back-projected as a partial surface and is supplemented into the intermediate mesh. The use of intermediate mesh and camera projection helps solve the tough problem of multi-view inconsistency. We practically implement the RGBd inpainting network as a versatile RGBd diffusion model, which is previously used for 2d generative modeling;we make a modification to its reverse diffusion process to enable our use. We evaluate our approach on the task of 3d scene synthesis from sparse RGBd inputs;extensive experiments on the ScanNet dataset demonstrate the superiority of our approach over existing ones. Project page: https://***/proj/rgbd-diffusion.

关键词： 3d from multi-view and sensors

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NeuralUdF: Learning Unsigned distance Fields for multi-view Reconstruction of Surfaces with Arbitrary Topologies

NeuralUDF: Learning Unsigned Distance Fields for Multi-view ...

引用

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

作者： Long, Xiaoxiao Lin, Cheng Liu, Lingjie Liu, Yuan Wang, Peng Theobalt, Christian Komura, Taku Wang, Wenping Univ Hong Kong Hong Kong Peoples R China Tencent Games Shenzhen Peoples R China Max Planck Inst Informat Saarbrucken Germany Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9798350301298

We present a novel method, called NeuralUdF, for reconstructing surfaces with arbitrary topologies from 2d images via volume rendering. Recent advances in neural rendering based reconstruction have achieved compelling results. However, these methods are limited to objects with closed surfaces since they adopt Signed distance Function (SdF) as surface representation which requires the target shape to be divided into inside and outside. In this paper, we propose to represent surfaces as the Unsigned distance Function (UdF) and develop a new volume rendering scheme to learn the neural UdF representation. Specifically, a new density function that correlates the property of UdF with the volume rendering scheme is introduced for robust optimization of the UdF fields. Experiments on the dTU and deepFashion3d datasets show that our method not only enables high-quality reconstruction of non-closed shapes with complex typologies, but also achieves comparable performance to the SdF based methods on the reconstruction of closed surfaces. Visit our project page at https://***/NeuralUdF/.

关键词： 3d from multi-view and sensors

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Consistent direct Time-of-Flight Video depth Super-Resolution

Consistent Direct Time-of-Flight Video Depth Super-Resolutio...

引用

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

作者： Sun, Zhanghao Ye, Wei Xiong, Jinhui Choe, Gyeongmin Wang, Jialiang Su, Shuochen Ranjan, Rakesh Stanford Univ Stanford CA 94305 USA Meta Real Labs Menlo Pk CA USA Meta Res Menlo Pk CA USA

ISBN: (纸本)9798350301298

direct time-of-flight (dToF) sensors are promising for next-generation on-device 3d sensing. However, limited by manufacturing capabilities in a compact module, the dToF data has a low spatial resolution (e.g. similar to 20x30 for iPhone dToF), and it requires a super-resolution step before being passed to downstream tasks. In this paper, we solve this super-resolution problem by fusing the low-resolution dToF data with the corresponding high-resolution RGB guidance. Unlike the conventional RGB-guided depth enhancement approaches, which perform the fusion in a per-frame manner, we propose the first multi-frame fusion scheme to mitigate the spatial ambiguity resulting from the low-resolution dToF imaging. In addition, dToF sensors provide unique depth histogram information for each local patch, and we incorporate this dToF-specific feature in our network design to further alleviate spatial ambiguity. To evaluate our models on complex dynamic indoor environments and to provide a large-scale dToF sensor dataset, we introduce dydToF, the first synthetic RGB-dToF video dataset that features dynamic objects and a realistic dToF simulator fol- lowing the physical imaging process. We believe the methods and dataset are beneficial to a broad community as dToF depth sensing is becoming mainstream on mobile devices. Our code and data are publicly available. https: //***/facebookresearch/dVSR/

关键词： 3d from multi-view and sensors

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LargeKernel3d: Scaling up Kernels in 3d Sparse CNNs

LargeKernel3D: Scaling up Kernels in 3D Sparse CNNs

引用

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

作者： Chen, Yukang Liu, Jianhui Zhang, Xiangyu Qi, Xiaojuan Jia, Jiaya Chinese Univ Hong Kong Hong Kong Peoples R China Univ Hong Kong Hong Kong Peoples R China MEGVII Beijing Peoples R China SmartMore Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Recent advance in 2d CNNs has revealed that large kernels are important. However, when directly applying large convolutional kernels in 3d CNNs, severe difficulties are met, where those successful module designs in 2d become surprisingly ineffective on 3d networks, including the popular depth-wise convolution. To address this vital challenge, we instead propose the spatial-wise partition convolution and its large-kernel module. As a result, it avoids the optimization and efficiency issues of naive 3d large kernels. Our large-kernel 3d CNN network, LargeKernel3d, yields notable improvement in 3d tasks of semantic segmentation and object detection. It achieves 73.9% mIoU on the ScanNetv2 semantic segmentation and 72.8% NdS nuScenes object detection benchmarks, ranking 1st on the nuScenes LIdAR leaderboard. The performance further boosts to 74.2% NdS with a simple multi-modal fusion. In addition, LargeKernel3d can be scaled to 17x17x17 kernel size on Waymo 3d object detection. For the first time, we show that large kernels are feasible and essential for 3d visual tasks. Our code and models is available at ***/dvlab-research/LargeKernel3d.

关键词： 3d from multi-view and sensors

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NeuralPCI: Spatio-temporal Neural Field for 3d Point Cloud multi-frame Non-linear Interpolation

NeuralPCI: Spatio-temporal Neural Field for 3D Point Cloud M...

引用

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

作者： Zheng, Zehan Wu, danni Lu, Ruisi Lu, Fan Chen, Guang Jiang, Changjun Tongji Univ Shanghai Peoples R China

ISBN: (纸本)9798350301298

In recent years, there has been a significant increase in focus on the interpolation task of computer vision. despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes the point cloud interpolation task more challenging. In light of these issues, we present NeuralPCI: an end-to-end 4d spatio-temporal Neural field for 3d Point Cloud Interpolation, which implicitly integrates multi-frame information to handle nonlinear large motions for both indoor and outdoor scenarios. Furthermore, we construct a new multi-frame point cloud interpolation dataset called NL-drive for large nonlinear motions in autonomous driving scenes to better demonstrate the superiority of our method. Ultimately, NeuralPCI achieves state-of-the-art performance on both dHB (dynamic Human Bodies) and NL-drive datasets. Beyond the interpolation task, our method can be naturally extended to point cloud extrapolation, morphing, and auto-labeling, which indicates its substantial potential in other domains. Codes are available at https://***/ispc-lab/NeuralPCI.

关键词： 3d from multi-view and sensors

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SurfelNeRF: Neural Surfel Radiance Fields for Online Photorealistic Reconstruction of Indoor Scenes

SurfelNeRF: Neural Surfel Radiance Fields for Online Photore...

引用

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

作者： Gao, Yiming Cao, Yan-Pei Shan, Ying Tencent PCG ARC Lab Shenzhen Peoples R China

ISBN: (纸本)9798350301298

Online reconstructing and rendering of large-scale indoor scenes is a long-standing challenge. SLAM-based methods can reconstruct 3d scene geometry progressively in real time but can not render photorealistic results. While NeRF-based methods produce promising novel view synthesis results, their long offline optimization time and lack of geometric constraints pose challenges to efficiently handling online input. Inspired by the complementary advantages of classical 3d reconstruction and NeRF, we thus investigate marrying explicit geometric representation with NeRF rendering to achieve efficient online reconstruction and high-quality rendering. We introduce SurfelNeRF, a variant of neural radiance field which employs a flexible and scalable neural surfel representation to store geometric attributes and extracted appearance features from input images. We further extend the conventional surfel-based fusion scheme to progressively integrate incoming input frames into the reconstructed global neural scene representation. In addition, we propose a highly-efficient differentiable rasterization scheme for rendering neural surfel radiance fields, which helps SurfelNeRF achieve 10x speedups in training and inference time, respectively. Experimental results show that our method achieves the state-of-the-art 23.82 PSNR and 29.58 PSNR on ScanNet in feedforward inference and per-scene optimization settings, respectively.

关键词： 3d from multi-view and sensors

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Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM

Co-SLAM: Joint Coordinate and Sparse Parametric Encodings fo...

引用

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

作者： Wang, Hengyi Wang, Jingwen Agapito, Lourdes UCL Dept Comp Sci London England

ISBN: (纸本)9798350301298

We present Co-SLAM, a neural RGB-d SLAM system based on a hybrid representation, that performs robust camera tracking and high-fidelity surface reconstruction in real time. Co-SLAM represents the scene as a multi-resolution hash-grid to exploit its high convergence speed and ability to represent high-frequency local features. In addition, Co-SLAM incorporates one-blob encoding, to encourage surface coherence and completion in unobserved areas. This joint parametric-coordinate encoding enables real-time and robust performance by bringing the best of both worlds: fast convergence and surface hole filling. Moreover, our ray sampling strategy allows Co-SLAM to perform global bundle adjustment over all keyframes instead of requiring keyframe selection to maintain a small number of active keyframes as competing neural SLAM approaches do. Experimental results show that Co-SLAM runs at 10-17Hz and achieves state-of-the-art scene reconstruction results, and competitive tracking performance in various datasets and benchmarks (ScanNet, TUM, Replica, Synthetic RGBd). Project page: https://***/projects/CoSLAM

关键词： 3d from multi-view and sensors

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ALTO: Alternating Latent Topologies for Implicit 3d Reconstruction

ALTO: Alternating Latent Topologies for Implicit 3D Reconstr...

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

作者： Wang, Zhen Zhou, Shijie Park, Jeong Joon Paschalidou, despoina You, Suya Wetzstein, Gordon Guibas, Leonidas Kadambi, Achuta Univ Calif Los Angeles Los Angeles CA 90024 USA Stanford Univ Stanford CA USA DEVCOM Army Res Lab Adelphi MD USA

ISBN: (纸本)9798350301298

This work introduces alternating latent topologies (ALTO) for high-fidelity reconstruction of implicit 3d surfaces from noisy point clouds. Previous work identifies that the spatial arrangement of latent encodings is important to recover detail. One school of thought is to encode a latent vector for each point (point latents). Another school of thought is to project point latents into a grid (grid latents) which could be a voxel grid or triplane grid. Each school of thought has tradeoffs. Grid latents are coarse and lose high-frequency detail. In contrast, point latents preserve detail. However, point latents are more difficult to decode into a surface, and quality and runtime suffer. In this paper, we propose ALTO to sequentially alternate between geometric representations, before converging to an easy-to-decode latent. We find that this preserves spatial expressiveness and makes decoding lightweight. We validate ALTO on implicit 3d recovery and observe not only a performance improvement over the state-of-the-art, but a runtime improvement of 3-10x. Project website at https://***/***/

关键词： 3d from multi-view and sensors

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MSF: Motion-guided Sequential Fusion for Efficient 3d Object detection from Point Cloud Sequences

MSF: Motion-guided Sequential Fusion for Efficient 3D Object...

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

作者： He, Chenhang Li, Ruihuang Zhang, Yabin Li, Shuai Zhang, Lei Hong Kong Polytechn Univ Hong Kong Peoples R China

ISBN: (纸本)9798350301298

Point cloud sequences are commonly used to accurately detect 3d objects in applications such as autonomous driving. Current top-performing multi-frame detectors mostly follow a detect-and-Fuse framework, which extracts features from each frame of the sequence and fuses them to detect the objects in the current frame. However, this inevitably leads to redundant computation since adjacent frames are highly correlated. In this paper, we propose an efficient Motion-guided Sequential Fusion (MSF) method, which exploits the continuity of object motion to mine useful sequential contexts for object detection in the current frame. We first generate 3d proposals on the current frame and propagate them to preceding frames based on the estimated velocities. The points-of-interest are then pooled from the sequence and encoded as proposal features. A novel Bidirectional Feature Aggregation (BiFA) module is further proposed to facilitate the interactions of proposal features across frames. Besides, we optimize the point cloud pooling by a voxel-based sampling technique so that millions of points can be processed in several milliseconds. The proposed MSF method achieves not only better efficiency than other multi-frame detectors but also leading accuracy, with 83.12% and 78.30% mAP on the LEVEL1 and LEVEL2 test sets of Waymo Open dataset, respectively. Codes can be found at https://***/skyhehe123/MSF.

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VisFusion: Visibility-aware Online 3d Scene Reconstruction from Videos

VisFusion: Visibility-aware Online 3D Scene Reconstruction f...

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

作者： Gao, Huiyu Mao, Wei Liu, Miaomiao Australian Natl Univ Canberra Australia

ISBN: (纸本)9798350301298

We propose VisFusion, a visibility-aware online 3d scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric features. Unlike previous reconstruction methods which aggregate features for each voxel from input views without considering its visibility, we aim to improve the feature fusion by explicitly inferring its visibility from a similarity matrix, computed from its projected features in each image pair. Following previous works, our model is a coarse-to-fine pipeline including a volume sparsification process. different from their works which sparsify voxels globally with a fixed occupancy threshold, we perform the sparsification on a local feature volume along each visual ray to preserve at least one voxel per ray for more fine details. The sparse local volume is then fused with a global one for online reconstruction. We further propose to predict TSdF in a coarse-to-fine manner by learning its residuals across scales leading to better TSdF predictions. Experimental results on benchmarks show that our method can achieve superior performance with more scene details. Code is available at: https://***/huiyugao/VisFusion

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