Papers point cloud upsampling
“point cloud upsampling” 태그가 달린 논문 62편 · 필터 해제
SRMamba: Mamba for Super-Resolution of LiDAR Point Clouds
In recent years, range-view-based LiDAR point cloud super-resolution techniques attract significant attention as a low-cost method for generating higher-resolution point cloud data. However, due to the sparsity and irreg…
MambaPoint Cloud Super Resolutionpoint cloud upsamplingSuper-ResolutionNon-uniform Point Cloud Upsampling via Local Manifold Distribution
Existing learning-based point cloud upsampling methods often overlook the intrinsic data distribution charac?teristics of point clouds, leading to suboptimal results when handling sparse and non-uniform point clouds. We …
point cloud upsamplingSPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network
Point cloud upsampling aims to generate dense and uniformly distributed point sets from sparse point clouds. Existing point cloud upsampling methods typically approach the task as an interpolation problem. They achieve u…
point cloud upsamplingPoint 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 upsampli…
Image SegmentationMedical Image AnalysisMedical Image Segmentationpoint cloud upsampling+2Efficient Point Clouds Upsampling via Flow Matching
Diffusion models are a powerful framework for tackling ill-posed problems, with recent advancements extending their use to point cloud upsampling. Despite their potential, existing diffusion models struggle with ineffici…
point cloud upsamplingRepresentation Learning of Point Cloud Upsampling in Global and Local Inputs
In recent years, point cloud upsampling has been widely applied in fields such as 3D reconstruction. Our study investigates the factors influencing point cloud upsampling on both global and local levels through represent…
3D ReconstructionDecoderpoint cloud upsamplingRepresentation LearningPoint Cloud Upsampling Using Conditional Diffusion Module with Adaptive Noise Suppression
Point cloud upsampling can improve the quality of the initial point cloud, significantly enhancing the performance of downstream tasks such as classification and segmentation. Existing methods mostly focus on generat…
point cloud upsamplingEGP3D: Edge-guided Geometric Preserving 3D Point Cloud Super-resolution for RGB-D camera
Point clouds or depth images captured by current RGB-D cameras often suffer from low resolution, rendering them insufficient for applications such as 3D reconstruction and robots. Existing point cloud super-resolution (P…
3D ReconstructionPoint Cloud Super Resolutionpoint cloud upsamplingSuper-ResolutionPLATYPUS: Progressive Local Surface Estimator for Arbitrary-Scale Point Cloud Upsampling
3D point clouds are increasingly vital for applications like autonomous driving and robotics, yet the raw data captured by sensors often suffer from noise and sparsity, creating challenges for downstream tasks. Consequen…
Autonomous Drivingpoint cloud upsamplingJoint Point Cloud Upsampling and Cleaning with Octree-based CNNs
Recovering dense and uniformly distributed point clouds from sparse or noisy data remains a significant challenge. Recently, great progress has been made on these tasks, but usually at the cost of increasingly intricate …
point cloud upsamplingMBPU: A Plug-and-Play State Space Model for Point Cloud Upsamping with Fast Point Rendering
The task of point cloud upsampling (PCU) is to generate dense and uniform point clouds from sparse input captured by 3D sensors like LiDAR, holding potential applications in real yet is still a challenging task. Existing…
Mambapoint cloud upsamplingGaussianPU: A Hybrid 2D-3D Upsampling Framework for Enhancing Color Point Clouds via 3D Gaussian Splatting
Dense colored point clouds enhance visual perception and are of significant value in various robotic applications. However, existing learning-based point cloud upsampling methods are constrained by computational resource…
3DGSGPUImage Restorationpoint cloud upsampling+2GET-UP: GEomeTric-aware Depth Estimation with Radar Points UPsampling
Depth estimation plays a pivotal role in autonomous driving, facilitating a comprehensive understanding of the vehicle's 3D surroundings. Radar, with its robustness to adverse weather conditions and capability to measure…
Autonomous DrivingDepth Estimationpoint 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 upsamplingArbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with Latent Geometric-Consistent Learning
Recently, arbitrary-scale point cloud upsampling mechanism became increasingly popular due to its efficiency and convenience for practical applications. To achieve this, most previous approaches formulate it as a problem…
point cloud upsamplingSPU-PMD: Self-Supervised Point Cloud Upsampling via Progressive Mesh Deformation
Despite the success of recent upsampling approaches generating high-resolution point sets with uniform distribution and meticulous structures is still challenging. Unlike existing methods that only take spatial infor…
Descriptivepoint cloud upsamplingRepKPU: Point Cloud Upsampling with Kernel Point Representation and Deformation
In this work we present RepKPU an efficient network for point cloud upsampling. We propose to promote upsampling performance by exploiting better shape representation and point generation strategy. Inspired by KPConv…
point cloud upsamplingLearning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling
Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud, which plays a critical role in 3D computer vision. Previous methods typically split a sparse point cloud into …
point cloud upsamplingA Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling
Point cloud upsampling (PCU) enriches the representation of raw point clouds, significantly improving the performance in downstream tasks such as classification and reconstruction. Most of the existing point cloud upsamp…
Denoisingpoint cloud upsamplingTest-Time Augmentation for 3D Point Cloud Classification and Segmentation
Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low poi…
3D Point Cloud ClassificationData AugmentationPoint Cloud Classificationpoint cloud upsampling+1