b'Zhizhong Han'

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...

0 Qing Li, et al. ∙

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...

0 Chao Chen, et al. ∙

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...

0 Junsheng Zhou, et al. ∙

research

∙ 08/21/2023

Coordinate Quantized Neural Implicit Representations for Multi-view Reconstruction

In recent years, huge progress has been made on learning neural implicit...

0 Sijia Jiang, et al. ∙

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...

0 Baorui Ma, et al. ∙

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...

0 Baorui Ma, et al. ∙

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 ...

0 Qing Li, et al. ∙

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 ...

0 Xinhai Liu, et al. ∙

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...

0 Chao Chen, et al. ∙

research

∙ 11/30/2022

NeAF: Learning Neural Angle Fields for Point Normal Estimation

Normal estimation for unstructured point clouds is an important task in ...

0 Shujuan Li, et al. ∙

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 ...

0 Qing Li, et al. ∙

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...

0 Junsheng Zhou, et al. ∙

research

∙ 08/14/2022

Fast Learning Radiance Fields by Shooting Much Fewer Rays

Learning radiance fields has shown remarkable results for novel view syn...

7 Wenyuan Zhang, et al. ∙

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...

1 Baorui Ma, et al. ∙

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...

6 Baorui Ma, et al. ∙

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...

0 Junsheng Zhou, et al. ∙

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...

0 Junsheng Zhou, et al. ∙

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...

0 Tianyang Li, et al. ∙

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...

0 Peng Xiang, et al. ∙

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...

0 Peng Xiang, et al. ∙

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...

18 Liang Pan, et al. ∙

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...

0 Peng Xiang, et al. ∙

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...

0 Chen Chao, et al. ∙

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...

2 Zhizhong Han, et al. ∙

research

∙ 07/05/2021

Part2Word: Learning Joint Embedding of Point Clouds and Text by Matching Parts to Words

It is important to learn joint embedding for 3D shapes and text in diffe...

0 Chuan Tang, et al. ∙

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...

0 Zhizhong Han, et al. ∙

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...

6 Xinhai Liu, et al. ∙

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 ...

0 Peng Xiang, et al. ∙

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...

8 Baorui Ma, et al. ∙

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...

2 Zhizhong Han, et al. ∙

research

∙ 05/26/2020

Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions

Fine-grained 3D shape classification is important and research challengi...

5 Xinhai Liu, et al. ∙

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...

11 Tianyang Li, et al. ∙

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...

0 Xinhai Liu, et al. ∙

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...

0 Xinhai Liu, et al. ∙

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-...

21 Zhizhong Han, et al. ∙

research

∙ 12/22/2019

Learning to Generate Dense Point Clouds with Textures on Multiple Categories

3D reconstruction from images is a core problem in computer vision. With...

21 Tao Hu, et al. ∙

research

∙ 12/15/2019

SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization

We propose SDFDiff, a novel approach for image-based shape optimization ...

53 Yue Jiang, et al. ∙

research

∙ 11/28/2019

3D Shape Completion with Multi-view Consistent Inference

3D shape completion is important to enable machines to perceive the comp...

19 Tao Hu, et al. ∙

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...

2 Han Liu, et al. ∙

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...

17 Zhizhong Han, et al. ∙

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 ...

2 Xinhai Liu, et al. ∙

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...

1 Zhizhong Han, et al. ∙

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-...

0 Zhizhong Han, et al. ∙

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...

2 Zhizhong Han, et al. ∙

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 ...

0 Zhizhong Han, et al. ∙

research

∙ 04/17/2019

Render4Completion: Synthesizing Multi-view Depth Maps for 3D Shape Completion

We propose a novel approach for 3D shape completion by synthesizing mult...

0 Tao Hu, et al. ∙

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...

0 Zhizhong Han, et al. ∙

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...

8 Zhizhong Han, et al. ∙

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...

10 Zhizhong Han, et al. ∙

research

∙ 11/06/2018

Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network

Exploring contextual information in the local region is important for sh...

2 Xinhai Liu, et al. ∙

Zhizhong Han

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