Search Results for author: Shanlin Sun

Found 17 papers, 4 papers with code

Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning

no code implementations4 Mar 2024 Tung Le, Khai Nguyen, Shanlin Sun, Nhat Ho, Xiaohui Xie

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.

Object Tracking valid

Adaptive Image Registration: A Hybrid Approach Integrating Deep Learning and Optimization Functions for Enhanced Precision

no code implementations27 Nov 2023 Gabriel De Araujo, Shanlin Sun, Xiaohui Xie

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.

Image Registration

CVTHead: One-shot Controllable Head Avatar with Vertex-feature Transformer

1 code implementation11 Nov 2023 Haoyu Ma, Tong Zhang, Shanlin Sun, Xiangyi Yan, Kun Han, Xiaohui Xie

Reconstructing personalized animatable head avatars has significant implications in the fields of AR/VR.

Neural Rendering

Light Field Diffusion for Single-View Novel View Synthesis

no code implementations20 Sep 2023 Yifeng Xiong, Haoyu Ma, Shanlin Sun, Kun Han, Hao Tang, Xiaohui Xie

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.

Denoising Novel View Synthesis +1

Hybrid-CSR: Coupling Explicit and Implicit Shape Representation for Cortical Surface Reconstruction

no code implementations23 Jul 2023 Shanlin Sun, Thanh-Tung Le, Chenyu You, Hao Tang, Kun Han, Haoyu Ma, Deying Kong, Xiangyi Yan, Xiaohui Xie

We present Hybrid-CSR, a geometric deep-learning model that combines explicit and implicit shape representations for cortical surface reconstruction.

Surface Reconstruction

Hybrid Neural Diffeomorphic Flow for Shape Representation and Generation via Triplane

no code implementations4 Jul 2023 Kun Han, Shanlin Sun, Xiaohui Xie

Deep Implicit Functions (DIFs) have gained popularity in 3D computer vision due to their compactness and continuous representation capabilities.

3D Shape Generation 3D Shape Representation +1

Diffeomorphic Mesh Deformation via Efficient Optimal Transport for Cortical Surface Reconstruction

no code implementations27 May 2023 Tung Le, Khai Nguyen, Shanlin Sun, Kun Han, Nhat Ho, Xiaohui Xie

The metric is defined by sliced Wasserstein distance on meshes represented as probability measures that generalize the set-based approach.

Surface Reconstruction

MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

no code implementations8 Apr 2023 Kun Han, Yifeng Xiong, Chenyu You, Pooya Khosravi, Shanlin Sun, Xiangyi Yan, James Duncan, Xiaohui Xie

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.

Image Segmentation Medical Image Segmentation +2

Localized Region Contrast for Enhancing Self-Supervised Learning in Medical Image Segmentation

no code implementations6 Apr 2023 Xiangyi Yan, Junayed Naushad, Chenyu You, Hao Tang, Shanlin Sun, Kun Han, Haoyu Ma, James Duncan, Xiaohui Xie

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.

Contrastive Learning Image Segmentation +5

Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

1 code implementation22 Sep 2022 Deying Kong, Linguang Zhang, Liangjian Chen, Haoyu Ma, Xiangyi Yan, Shanlin Sun, Xingwei Liu, Kun Han, Xiaohui Xie

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

no code implementations7 Jun 2022 Shanlin Sun, Kun Han, Hao Tang, Deying Kong, Junayed Naushad, Xiangyi Yan, Xiaohui Xie

Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images.

Image Registration Medical Image Registration +1

Topology-Preserving Shape Reconstruction and Registration via Neural Diffeomorphic Flow

1 code implementation CVPR 2022 Shanlin Sun, Kun Han, Deying Kong, Hao Tang, Xiangyi Yan, Xiaohui Xie

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.

Organ Segmentation Template Matching

Diffeomorphic Image Registration with Neural Velocity Field

no code implementations25 Feb 2022 Kun Han, Shanlin Sun, Xiangyi Yan, Chenyu You, Hao Tang, Junayed Naushad, Haoyu Ma, Deying Kong, Xiaohui Xie

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.

Image Registration

AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation

no code implementations20 Oct 2021 Xiangyi Yan, Hao Tang, Shanlin Sun, Haoyu Ma, Deying Kong, Xiaohui Xie

One has to either downsample the image or use cropped local patches to reduce GPU memory usage, which limits its performance.

Image Segmentation Medical Image Segmentation +3

Recurrent Mask Refinement for Few-Shot Medical Image Segmentation

1 code implementation ICCV 2021 Hao Tang, Xingwei Liu, Shanlin Sun, Xiangyi Yan, Xiaohui Xie

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.

Few-Shot Learning Image Segmentation +4

Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation

no code implementations16 Dec 2020 Hao Tang, Xingwei Liu, Kun Han, Shanlin Sun, Narisu Bai, Xuming Chen, Huang Qian, Yong liu, Xiaohui Xie

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.

Image Segmentation Organ Segmentation +2

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