Papers Point Cloud Super Resolution
“Point Cloud Super Resolution” 태그가 달린 논문 16편 · 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-ResolutionR2LDM: An Efficient 4D Radar Super-Resolution Framework Leveraging Diffusion Model
We introduce R2LDM, an innovative approach for generating dense and accurate 4D radar point clouds, guided by corresponding LiDAR point clouds. Instead of utilizing range images or bird's eye view (BEV) images, we repres…
object-detectionObject DetectionPoint cloud reconstructionPoint Cloud Registration+2EGP3D: 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-ResolutionDiffusion-Based Point Cloud Super-Resolution for mmWave Radar Data
The millimeter-wave radar sensor maintains stable performance under adverse environmental conditions, making it a promising solution for all-weather perception tasks, such as outdoor mobile robotics. However, the radar p…
Point Cloud Super ResolutionSuper-ResolutionPDF: Point Diffusion Implicit Function for Large-scale Scene Neural Representation
Recent advances in implicit neural representations have achieved impressive results by sampling and fusing individual points along sampling rays in the sampling space. However, due to the explosively growing sampling spa…
NeRFNovel View SynthesisPoint Cloud Super ResolutionSuper-ResolutionLightweight super resolution network for point cloud geometry compression
This paper presents an approach for compressing point cloud geometry by leveraging a lightweight super-resolution network. The proposed method involves decomposing a point cloud into a base point cloud and the interpolat…
DecoderPoint cloud reconstructionPoint Cloud Super ResolutionSuper-ResolutionASUR3D: Arbitrary Scale Upsampling and Refinement of 3D Point Clouds using Local Occupancy Fields
In this paper, we introduce ASUR3D, a novel methodology for the arbitrary-scale upsampling of 3D point clouds employing Local Occupancy Representation. Our proposed implicit occupancy representation enables efficient poi…
3D Reconstruction3D Shape Generation3D Shape RepresentationPoint Cloud Super Resolution+1TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards Upsampling
In this paper, we propose TP-NoDe, a novel Topology-aware Progressive Noising and Denoising technique for 3D point cloud upsampling. TP-NoDe revisits the traditional method of upsampling of the point cloud by introducing…
DenoisingPoint Cloud Super Resolutionpoint cloud upsamplingPU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention
Recently, research using point clouds has been increasing with the development of 3D scanner technology. According to this trend, the demand for high-quality point clouds is increasing, but there is still a problem with …
Point cloud reconstructionPoint Cloud Super ResolutionFrequency-Selective Mesh-to-Mesh Resampling for Color Upsampling of Point Clouds
With the increased use of virtual and augmented reality applications, the importance of point cloud data rises. High-quality capturing of point clouds is still expensive and thus, the need for point cloud super-resolutio…
Point Cloud Super Resolutionpoint cloud upsamplingSuper-ResolutionMeta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud
Point cloud upsampling is vital for the quality of the mesh in three-dimensional reconstruction. Recent research on point cloud upsampling has achieved great success due to the development of deep learning. However, the …
Point Cloud Super Resolutionpoint cloud upsamplingPUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
This paper addresses the problem of generating uniform dense point clouds to describe the underlying geometric structures from given sparse point clouds. Due to the irregular and unordered nature, point cloud densificati…
Point Cloud Super Resolutionpoint cloud upsamplingSuper-ResolutionSurface ReconstructionPU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuf…
3D ReconstructionPoint Cloud Super Resolutionpoint cloud upsamplingPU-GAN: a Point Cloud Upsampling Adversarial Network
Point clouds acquired from range scans are often sparse, noisy, and non-uniform. This paper presents a new point cloud upsampling network called PU-GAN, which is formulated based on a generative adversarial network (GAN)…
3D ReconstructionGenerative Adversarial NetworkPoint Cloud Super Resolutionpoint cloud upsamplingPatch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super…
Point Cloud Super ResolutionPoint Set UpsamplingSuper-ResolutionSurface ReconstructionPU-Net: Point Cloud Upsampling Network
Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud upsampling technique. The key idea is to le…
Point Cloud Super Resolutionpoint cloud upsampling