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computer-vision-11

4D reconstruction

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues

Chen Yang, Kailing Wang, Yuehao Wang, Xiaokang Yang, Wei-Ming Shen•Tue May 30 2023

LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting, leading to a compact memory footprint and significantly accelerated optimization, and proposes a novel sample scheme to boost optimization and improve performance in regions with tool occlusion and large motions.

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Iterative inversion of deformation vector fields with feedback control

A. Dubey, A. Iliopoulos, Xiaobai Sun, F. Yin, L. Ren•Wed Oct 26 2016

The NTDC‐adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms, and show remarkable quantitative agreement with analysis‐based predictions.

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Temporal Interpolation via Motion Field Prediction

E. Konukoglu, Lin Zhang, Neerav Karani, C. Tanner•Wed Apr 11 2018

This work proposes a convolutional neural network (CNN) based method for temporal interpolation via motion field prediction and shows that these motion fields can be used to halve the number of registrations required during 4D reconstruction, thus substantially reducing the reconstruction time.

8 0

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Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction

K. Jia, Dan Xu, Jiapeng Tang, Lei Zhang•Mon Mar 29 2021

This work presents a novel pipeline to learn a temporal evolution of the 3D human shape through spatially continuous transformation functions among cross-frame occupancy fields via explicitly learning continuous displacement vector fields from robust spatio-temporal shape representations.

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LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling

Yinda Zhang, Yanwei Fu, Boyan Jiang, Xinlin Ren, Mingsong Dou, Xiangyang Xue•Wed Aug 17 2022

This paper proposes a novel Local 4D implicit Representation for Dynamic clothed human, named LoRD, which has the merits of both 4D human modeling and local representation, and enables high-fidelity reconstruction with detailed surface deformations, such as clothing wrinkles.

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4D Myocardium Reconstruction with Decoupled Motion and Shape Model

Xiaohan Yuan, Cong Liu, Yangang Wang•Sat Aug 26 2023

A 4D reconstruction method that decouples motion and shape, which can predict the inter-/intra- shape and motion estimation from a given sparse point cloud sequence obtained from limited slices is proposed and proposed.

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ResFields: Residual Neural Fields for Spatiotemporal Signals

M. Pollefeys, Marko Mihajlovic, Sergey Prokudin, Siyu Tang•Tue Sep 05 2023

This paper proposes a novel class of networks specifically designed to effectively represent complex temporal signals, dubbed ResFields, and proposes a matrix factorization technique to reduce the number of trainable parameters and enhance generalization capabilities.

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LiDAR4D: Dynamic Neural Fields for Novel Space-Time View LiDAR Synthesis

Fan Lu, Zehan Zheng, Weiyi Xue, Guang Chen, Changjun Jiang•Tue Apr 02 2024

The proposed LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis, designs a 4D hybrid representation combined with multi-planar and grid features to achieve effective reconstruction in a coarse-to-fine manner and introduces geometric constraints derived from point clouds to improve temporal consistency.

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4D reconstruction | State-of-the-Art