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Point Cloud Super Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Point cloud super-resolution is a fundamental problem for 3D reconstruction and 3D data understanding. It takes a low-resolution (LR) point cloud as input and generates a high-resolution (HR) point cloud with rich details
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
R2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model
EGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera
Diffusion-Based Point Cloud Super-Resolution for mmWave Radar Data
PDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation
Lightweight super resolution network for point cloud geometry compression
PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention
Frequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds
Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
PU-GAN: a Point Cloud Upsampling Adversarial Network
Patch-based Progressive 3D Point Set Upsampling
PU-Net: Point Cloud Upsampling Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.