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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.
Lightweight super resolution network for point cloud geometry compression
Polyhedral Surface: Self-supervised Point Cloud Reconstruction Based on Polyhedral Surface
Automatic Reverse Engineering: Creating computer-aided design (CAD) models from multi-view images
SculptBot: Pre-Trained Models for 3D Deformable Object Manipulation
StairNetV3: Depth-aware Stair Modeling using Deep Learning
Variational Autoencoding of Dental Point Clouds
PAPR: Proximity Attention Point Rendering
RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
Tensorformer: Normalized Matrix Attention Transformer for High-quality Point Cloud Reconstruction
APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction
Energy-Based Sliced Wasserstein Distance
3D GANs and Latent Space: A comprehensive survey
StarNet: Style-Aware 3D Point Cloud Generation
Category-level Shape Estimation for Densely Cluttered Objects
Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
PointInverter: Point Cloud Reconstruction and Editing via a Generative Model with Shape Priors
3D Reconstruction of Multiple Objects by mmWave Radar on UAV
Self-Distillation for Unsupervised 3D Domain Adaptation
Cross-modal Learning for Image-Guided Point Cloud Shape Completion
Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations
PU-MFA : Point Cloud Up-sampling via Multi-scale Features Attention
Masked Surfel Prediction for Self-Supervised Point Cloud Learning
Masked Autoencoders in 3D Point Cloud Representation Learning
ANISE: Assembly-based Neural Implicit Surface rEconstruction
Bone marrow sparing for cervical cancer radiotherapy on multimodality medical images
Autoregressive 3D Shape Generation via Canonical Mapping
Self-Supervised Point Cloud Registration with Deep Versatile Descriptors
CP-Net: Contour-Perturbed Reconstruction Network for Self-Supervised Point Cloud Learning
Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
Deep Point Cloud Reconstruction
PointMixer: MLP-Mixer for Point Cloud Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.