Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling

• URL: http://arxiv.org/abs/2406.18422v1
• Date: Wed, 26 Jun 2024 15:18:20 GMT
• Title: Repeat and Concatenate: 2D to 3D Image Translation with 3D to 3D Generative Modeling
• Authors: Abril Corona-Figueroa, Hubert P. H. Shum, Chris G. Willcocks,
• Abstract summary: This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space lose valuable signal information during latent encoding. Instead, we simply repeat and the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin U
• Score: 14.341099905684844
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D views in the latent space, lose valuable signal information during latent encoding. Instead, we simply repeat and concatenate the 2D views into higher-channel 3D volumes and approach the 3D reconstruction challenge as a straightforward 3D to 3D generative modeling problem, sidestepping several complex modeling issues. This method enables the reconstructed 3D volume to retain valuable information from the 2D inputs, which are passed between channel states in a Swin UNETR backbone. Our approach applies neural optimal transport, which is fast and stable to train, effectively integrating signal information across multiple views without the requirement for precise alignment; it produces non-collapsed reconstructions that are highly faithful to the 2D views, even after limited training. We demonstrate correlated results, both qualitatively and quantitatively, having trained our model on a single dataset and evaluated its generalization ability across six datasets, including out-of-distribution samples.

Related papers

• Zero-Shot Dual-Path Integration Framework for Open-Vocabulary 3D Instance Segmentation [19.2297264550686]
  Open-vocabulary 3D instance segmentation transcends traditional closed-vocabulary methods. We introduce Zero-Shot Dual-Path Integration Framework that equally values the contributions of both 3D and 2D modalities. Our framework, utilizing pre-trained models in a zero-shot manner, is model-agnostic and demonstrates superior performance on both seen and unseen data.
  arXiv  Detail & Related papers  (2024-08-16T07:52:00Z)
• Sculpt3D: Multi-View Consistent Text-to-3D Generation with Sparse 3D Prior [57.986512832738704]
  We present a new framework Sculpt3D that equips the current pipeline with explicit injection of 3D priors from retrieved reference objects without re-training the 2D diffusion model. Specifically, we demonstrate that high-quality and diverse 3D geometry can be guaranteed by keypoints supervision through a sparse ray sampling approach. These two decoupled designs effectively harness 3D information from reference objects to generate 3D objects while preserving the generation quality of the 2D diffusion model.
  arXiv  Detail & Related papers  (2024-03-14T07:39:59Z)
• Text-to-3D Generation with Bidirectional Diffusion using both 2D and 3D priors [16.93758384693786]
  Bidirectional Diffusion(BiDiff) is a unified framework that incorporates both a 3D and a 2D diffusion process. Our model achieves high-quality, diverse, and scalable 3D generation.
  arXiv  Detail & Related papers  (2023-12-07T10:00:04Z)
• Geometry-Aware Attenuation Learning for Sparse-View CBCT Reconstruction [53.93674177236367]
  Cone Beam Computed Tomography (CBCT) plays a vital role in clinical imaging. Traditional methods typically require hundreds of 2D X-ray projections to reconstruct a high-quality 3D CBCT image. This has led to a growing interest in sparse-view CBCT reconstruction to reduce radiation doses. We introduce a novel geometry-aware encoder-decoder framework to solve this problem.
  arXiv  Detail & Related papers  (2023-03-26T14:38:42Z)
• Deep Generative Models on 3D Representations: A Survey [81.73385191402419]
  Generative models aim to learn the distribution of observed data by generating new instances. Recently, researchers started to shift focus from 2D to 3D space. representing 3D data poses significantly greater challenges.
  arXiv  Detail & Related papers  (2022-10-27T17:59:50Z)
• To The Point: Correspondence-driven monocular 3D category reconstruction [39.811816510186475]
  To The Point (TTP) is a method for reconstructing 3D objects from a single image using 2D to 3D correspondences learned from weak supervision. We replace CNN-based regression of camera pose and non-rigid deformation and obtain substantially more accurate 3D reconstructions.
  arXiv  Detail & Related papers  (2021-06-10T11:21:14Z)
• 3D-to-2D Distillation for Indoor Scene Parsing [78.36781565047656]
  We present a new approach that enables us to leverage 3D features extracted from large-scale 3D data repository to enhance 2D features extracted from RGB images. First, we distill 3D knowledge from a pretrained 3D network to supervise a 2D network to learn simulated 3D features from 2D features during the training. Second, we design a two-stage dimension normalization scheme to calibrate the 2D and 3D features for better integration. Third, we design a semantic-aware adversarial training model to extend our framework for training with unpaired 3D data.
  arXiv  Detail & Related papers  (2021-04-06T02:22:24Z)
• Spatial Context-Aware Self-Attention Model For Multi-Organ Segmentation [18.76436457395804]
  Multi-organ segmentation is one of most successful applications of deep learning in medical image analysis. Deep convolutional neural nets (CNNs) have shown great promise in achieving clinically applicable image segmentation performance on CT or MRI images. We propose a new framework for combining 3D and 2D models, in which the segmentation is realized through high-resolution 2D convolutions.
  arXiv  Detail & Related papers  (2020-12-16T21:39:53Z)
• Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation [87.54570024320354]
  State-of-the-art methods for large-scale driving-scene LiDAR semantic segmentation often project and process the point clouds in the 2D space. A straightforward solution to tackle the issue of 3D-to-2D projection is to keep the 3D representation and process the points in the 3D space. We develop a 3D cylinder partition and a 3D cylinder convolution based framework, termed as Cylinder3D, which exploits the 3D topology relations and structures of driving-scene point clouds.
  arXiv  Detail & Related papers  (2020-08-04T13:56:19Z)
• Exemplar Fine-Tuning for 3D Human Model Fitting Towards In-the-Wild 3D Human Pose Estimation [107.07047303858664]
  Large-scale human datasets with 3D ground-truth annotations are difficult to obtain in the wild. We address this problem by augmenting existing 2D datasets with high-quality 3D pose fits. The resulting annotations are sufficient to train from scratch 3D pose regressor networks that outperform the current state-of-the-art on in-the-wild benchmarks.
  arXiv  Detail & Related papers  (2020-04-07T20:21:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.