Point Cloud Upsampling as Statistical Shape Model for Pelvic
We propose a novel framework that integrates medical image segmentation and point cloud upsampling for accurate shape reconstruction of pelvic models. Using the SAM-Med3D model for segmentation and a point cloud upsampling network trained on the MedShapeNet dataset, our method transforms sparse medical imaging data into high-resolution 3D bone models. This framework leverages prior knowledge of anatomical shapes, achieving smoother and more complete reconstructions. Quantitative evaluations using metrics such as Chamfer Distance etc, demonstrate the effectiveness of the point cloud upsampling in pelvic model. Our approach offers potential applications in reconstructing other skeletal structures, providing a robust solution for medical image analysis and statistical shape modeling.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationMedical Image AnalysisMedical Image Segmentationpoint cloud upsamplingSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling
Completing an unordered partial point cloud is a challenging task. Existing approaches that rely on decoding a latent feature to recover the complete shape, often lead to the completed point cloud being over-smoothing, l…
Point Cloud CompletionSSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering
Point clouds obtained from 3D sensors are usually sparse. Existing methods mainly focus on upsampling sparse point clouds in a supervised manner by using dense ground truth point clouds. In this paper, we propose a self-…
point cloud upsamplingRethinking Data Input for Point Cloud Upsampling
In recent years, point cloud upsampling has been widely applied in fields such as 3D reconstruction and surface generation. However, existing point cloud upsampling inputs are all patch based, and there is no research di…
3D Reconstructionpoint cloud upsampling"Zero-Shot" Point Cloud Upsampling
Recent supervised point cloud upsampling methods are restricted by the size of training data and are limited in terms of covering all object shapes. Besides the challenges faced due to data acquisition, the networks also…
point cloud upsamplingSuper-ResolutionDMF-Net: Image-Guided Point Cloud Completion with Dual-Channel Modality Fusion and Shape-Aware Upsampling Transformer
In this paper we study the task of a single-view image-guided point cloud completion. Existing methods have got promising results by fusing the information of image into point cloud explicitly or implicitly. However, giv…
Point Cloud Completion