research
∙
09/17/2023
Neural Gradient Learning and Optimization for Oriented Point Normal Estimation
We propose Neural Gradient Learning (NGL), a deep learning approach to l...
share
research
∙
08/25/2023
GridPull: Towards Scalability in Learning Implicit Representations from 3D Point Clouds
Learning implicit representations has been a widely used solution for su...
research
∙
08/22/2023
Learning a More Continuous Zero Level Set in Unsigned Distance Fields through Level Set Projection
Latest methods represent shapes with open surfaces using unsigned distan...
research
∙
06/02/2023
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise Mapping
Learning signed distance functions (SDFs) from 3D point clouds is an imp...
research
∙
05/19/2023
Towards Better Gradient Consistency for Neural Signed Distance Functions via Level Set Alignment
Neural signed distance functions (SDFs) have shown remarkable capability...
research
∙
05/10/2023
SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds
We propose a novel method called SHS-Net for oriented normal estimation ...
research
∙
05/10/2023
D-Net: Learning for Distinctive Point Clouds by Self-Attentive Point Searching and Learnable Feature Fusion
Learning and selecting important points on a point cloud is crucial for ...
research
∙
04/02/2023
Robust Multiview Point Cloud Registration with Reliable Pose Graph Initialization and History Reweighting
In this paper, we present a new method for the multiview registration of...
research
∙
03/25/2023
Unsupervised Inference of Signed Distance Functions from Single Sparse Point Clouds without Learning Priors
It is vital to infer signed distance functions (SDFs) from 3D point clou...
research
∙
11/30/2022
NeAF: Learning Neural Angle Fields for Point Normal Estimation
Normal estimation for unstructured point clouds is an important task in ...
research
∙
10/13/2022
HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces
We propose a novel normal estimation method called HSurf-Net, which can ...
research
∙
10/06/2022
Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds
Surface reconstruction for point clouds is an important task in 3D compu...
research
∙
08/14/2022
Fast Learning Radiance Fields by Shooting Much Fewer Rays
Learning radiance fields has shown remarkable results for novel view syn...
research
∙
07/18/2022
Latent Partition Implicit with Surface Codes for 3D Representation
Deep implicit functions have shown remarkable shape modeling ability in ...
research
∙
04/23/2022
Surface Reconstruction from Point Clouds by Learning Predictive Context Priors
Surface reconstruction from point clouds is vital for 3D computer vision...
research
∙
04/22/2022
Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors
It is an important task to reconstruct surfaces from 3D point clouds. Cu...
research
∙
03/29/2022
3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow
Reconstructing 3D shape from a single 2D image is a challenging task, wh...
research
∙
03/26/2022
Self-Supervised Point Cloud Representation Learning with Occlusion Auto-Encoder
Learning representations for point clouds is an important task in 3D com...
research
∙
03/26/2022
Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds
Deep Implicit Function (DIF) has gained popularity as an efficient 3D sh...
research
∙
02/19/2022
PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths
Point cloud completion concerns to predict missing part for incomplete 3...
research
∙
02/18/2022
Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer
Most existing point cloud completion methods suffered from discrete natu...
research
∙
12/22/2021
Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results
As real-scanned point clouds are mostly partial due to occlusions and vi...
research
∙
11/23/2021
MFM-Net: Unpaired Shape Completion Network with Multi-stage Feature Matching
Unpaired 3D object completion aims to predict a complete 3D shape from a...
research
∙
08/10/2021
SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer
Point cloud completion aims to predict a complete shape in high accuracy...
research
∙
08/08/2021
Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching
Learning to generate 3D point clouds without 3D supervision is an import...
research
∙
08/08/2021
Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views
Unsupervised learning of global features for 3D shape analysis is an imp...
research
∙
03/14/2021
Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding
In this paper, we present a novel unpaired point cloud completion networ...
research
∙
12/08/2020
SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization
The task of point cloud upsampling aims to acquire dense and uniform poi...
research
∙
12/07/2020
PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths
The task of point cloud completion aims to predict the missing part for ...
research
∙
11/26/2020
Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces
Reconstructing continuous surfaces from 3D point clouds is a fundamental...
research
∙
07/12/2020
DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images
Differentiable renderers have been used successfully for unsupervised 3D...
research
∙
05/26/2020
Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
Fine-grained 3D shape classification is important and research challengi...
research
∙
05/08/2020
Point Cloud Completion by Skip-attention Network with Hierarchical Folding
Point cloud completion aims to infer the complete geometries for missing...
research
∙
03/18/2020
LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts
Learning discriminative feature directly on point clouds is still challe...
research
∙
03/18/2020
LRC-Net: Learning Discriminative Features on Point Clouds by EncodingLocal Region Contexts
Learning discriminative feature directly on point clouds is still challe...
research
∙
03/12/2020
SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates
Structure learning for 3D shapes is vital for 3D computer vision. State-...
research
∙
09/16/2019
MVDLite: A Light-weight Model View Definition Representation with Fast Validation for Building Information Model
Model View Definition (MVD) is the standard methodology to define the ex...
research
∙
09/16/2019
MVDLite: A Light-weight Representation of Model View Definition with Fast Validation for BIM Applications
Model View Definition (MVD) is the standard methodology to define the pa...
research
∙
09/13/2019
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Low-rank metric learning aims to learn better discrimination of data sub...
research
∙
08/29/2019
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules
Learning discriminative shape representation directly on point clouds is...
research
∙
08/02/2019
L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention
Auto-encoder is an important architecture to understand point clouds in ...
research
∙
07/31/2019
ShapeCaptioner: Generative Caption Network for 3D Shapes by Learning a Mapping from Parts Detected in Multiple Views to Sentences
3D shape captioning is a challenging application in 3D shape understandi...
research
∙
07/30/2019
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Unsupervised feature learning for point clouds has been vital for large-...
research
∙
05/18/2019
Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views
Deep learning has achieved remarkable results in 3D shape analysis by le...
research
∙
05/17/2019
3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention
Learning global features by aggregating information over multiple views ...
research
∙
04/17/2019
Adversarial Cross-Modal Retrieval via Learning and Transferring Single-Modal Similarities
Cross-modal retrieval aims to retrieve relevant data across different mo...
research
∙
11/07/2018
Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences
A recent method employs 3D voxels to represent 3D shapes, but this limit...
research
∙
11/07/2018
View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
In this paper we present a novel unsupervised representation learning ap...
research
∙
11/06/2018
