Papers with Code 아카이브
Point cloud reconstruction
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This task aims to solve inherent problems in raw point clouds: sparsity, noise, and irregularity.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Differentiable Spline Approximations
Visual Enhanced 3D Point Cloud Reconstruction from A Single Image
NURBS-Diff: A Differentiable Programming Module for NURBS
MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy
3D Surface Reconstruction From Multi-Date Satellite Images
Canonical Capsules: Self-Supervised Capsules in Canonical Pose
Set Prediction without Imposing Structure as Conditional Density Estimation
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks
Dense Hybrid Recurrent Multi-view Stereo Net with Dynamic Consistency Checking
From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
M^3VSNet: Unsupervised Multi-metric Multi-view Stereo Network
Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
Silhouette Guided Point Cloud Reconstruction beyond Occlusion
Deep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering
Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image
SO-Net: Self-Organizing Network for Point Cloud Analysis
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.