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Found 17 papers, 4 papers with code
Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning
In the realm of computer vision and graphics, accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking, registration, texture transfer, and statistical shape analysis.
Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision
Image registration has traditionally been done using two distinct approaches: learning based methods, relying on robust deep neural networks, and optimization-based methods, applying complex mathematical transformations to warp images accordingly.
CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer
Reconstructing personalized animatable head avatars has significant implications in the fields of AR/VR.
Light Field Diffusion for Single-View Novel View Synthesis
Starting from the camera pose matrices, LFD transforms them into light field encoding, with the same shape as the reference image, to describe the direction of each ray.
Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction
We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction.
Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane
Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision due to their compactness and continuous representation capabilities.
Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction
The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach.
MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation
Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks.
Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation
In this paper, we propose a novel contrastive learning framework that integrates Localized Region Contrast (LRC) to enhance existing self-supervised pre-training methods for medical image segmentation.
Identity-Aware Hand Mesh Estimation and Personalization from RGB Images
In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject.
Medical Image Registration via Neural Fields
Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images.
Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow
Recently DIFs-based methods have been proposed to handle shape reconstruction and dense point correspondences simultaneously, capturing semantic relationships across shapes of the same class by learning a DIFs-modeled shape template.
Diffeomorphic Image Registration with Neural Velocity Field
Here we propose a new optimization-based method named DNVF (Diffeomorphic Image Registration with Neural Velocity Field) which utilizes deep neural network to model the space of admissible transformations.
AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation
One has to either downsample the image or use cropped local patches to reduce GPU memory usage, which limits its performance.
Recurrent Mask Refinement for Few-Shot Medical Image Segmentation
Although having achieved great success in medical image segmentation, deep convolutional neural networks usually require a large dataset with manual annotations for training and are difficult to generalize to unseen classes.
Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation
State-of-the-art CNN segmentation models apply either 2D or 3D convolutions on input images, with pros and cons associated with each method: 2D convolution is fast, less memory-intensive but inadequate for extracting 3D contextual information from volumetric images, while the opposite is true for 3D convolution.
AttentionAnatomy: A unified framework for whole-body organs at risk segmentation using multiple partially annotated datasets
Organs-at-risk (OAR) delineation in computed tomography (CT) is an important step in Radiation Therapy (RT) planning.
