4D reconstruction
9 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in 4D reconstruction
Most implemented papers
Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues
Reconstructing deformable tissues from endoscopic stereo videos in robotic surgery is crucial for various clinical applications.
Iterative Inversion of Deformation Vector Fields with Feedback Control
Conclusion: Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control.
Temporal Interpolation via Motion Field Prediction
Temporal interpolation of navigator slices an be used to reduce the number of navigator acquisitions without degrading specificity in stacking.
Learning Parallel Dense Correspondence from Spatio-Temporal Descriptors for Efficient and Robust 4D Reconstruction
tangjiapeng/LPDC-Net
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CVPR 2021
This paper focuses on the task of 4D shape reconstruction from a sequence of point clouds.
LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling
BoyanJIANG/LoRD
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Recent progress in 4D implicit representation focuses on globally controlling the shape and motion with low dimensional latent vectors, which is prone to missing surface details and accumulating tracking error.
4D Myocardium Reconstruction with Decoupled Motion and Shape Model
yuan-xiaohan/4d-myocardium-reconstruction-with-decoupled-motion-and-shape-model
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ICCV 2023
Estimating the shape and motion state of the myocardium is essential in diagnosing cardiovascular diseases. However, cine magnetic resonance (CMR) imaging is dominated by 2D slices, whose large slice spacing challenges inter-slice shape reconstruction and motion acquisition. To address this problem, we propose 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.
ResFields: Residual Neural Fields for Spatiotemporal Signals
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in modeling complex 3D data, such as signed distance (SDFs) or radiance fields (NeRFs), via a single multi-layer perceptron (MLP).
Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Orthogonal Diffusion Models
Recent advancements in 3D generation are predominantly propelled by improvements in 3D-aware image diffusion models which are pretrained on Internet-scale image data and fine-tuned on massive 3D data, offering the capability of producing highly consistent multi-view images.
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis
ispc-lab/lidar4d
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In light of this, we propose LiDAR4D, a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis.
