Search Results for author: Zhizhong Han

Found 38 papers, 17 papers with code

Surface Reconstruction from Point Clouds by Learning Predictive Context Priors

1 code implementation23 Apr 2022 Baorui Ma, Yu-Shen Liu, Matthias Zwicker, Zhizhong Han

To reconstruct a surface at a specific query location at inference time, these methods then match the local reconstruction target by searching for the best match in the local prior space (by optimizing the parameters encoding the local context) at the given query location.

Surface Reconstruction

Reconstructing Surfaces for Sparse Point Clouds with On-Surface Priors

1 code implementation22 Apr 2022 Baorui Ma, Yu-Shen Liu, Zhizhong Han

Our key idea is to infer signed distances by pushing both the query projections to be on the surface and the projection distance to be the minimum.

3D Shape Reconstruction from 2D Images with Disentangled Attribute Flow

1 code implementation29 Mar 2022 Xin Wen, Junsheng Zhou, Yu-Shen Liu, Zhen Dong, Zhizhong Han

Reconstructing 3D shape from a single 2D image is a challenging task, which needs to estimate the detailed 3D structures based on the semantic attributes from 2D image.

3D Reconstruction 3D Shape Reconstruction

Self-Supervised Point Cloud Representation Learning with Occlusion Auto-Encoder

1 code implementation26 Mar 2022 Junsheng Zhou, Xin Wen, Yu-Shen Liu, Yi Fang, Zhizhong Han

Our key idea is to randomly occlude some local patches of the input point cloud and establish the supervision via recovering the occluded patches using the remaining visible ones.

Representation Learning

Learning Deep Implicit Functions for 3D Shapes with Dynamic Code Clouds

1 code implementation26 Mar 2022 Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, Zhizhong Han

However, the local codes are constrained at discrete and regular positions like grid points, which makes the code positions difficult to be optimized and limits their representation ability.

3D Shape Representation

PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths

1 code implementation19 Feb 2022 Xin Wen, Peng Xiang, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu

It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest.

Point Cloud Completion Representation Learning

Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer

no code implementations18 Feb 2022 Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Zhizhong Han

Most existing point cloud completion methods suffered from discrete nature of point clouds and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details.

Image Reconstruction Point Cloud Completion

SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer

1 code implementation ICCV 2021 Peng Xiang, Xin Wen, Yu-Shen Liu, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Zhizhong Han

However, previous methods usually suffered from discrete nature of point cloud and unstructured prediction of points in local regions, which makes it hard to reveal fine local geometric details on the complete shape.

Point Cloud Completion

Hierarchical View Predictor: Unsupervised 3D Global Feature Learning through Hierarchical Prediction among Unordered Views

no code implementations8 Aug 2021 Zhizhong Han, Xiyang Wang, Yu-Shen Liu, Matthias Zwicker

To mine highly discriminative information from unordered views, HVP performs a novel hierarchical view prediction over a view pair, and aggregates the knowledge learned from the predictions in all view pairs into a global feature.

Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching

1 code implementation8 Aug 2021 Chen Chao, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker

Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles.

Point Cloud Generation

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

no code implementations5 Jul 2021 Chuan Tang, Xi Yang, Bojian Wu, Zhizhong Han, Yi Chang

To resolve this issue, we propose a method to learn joint embedding of point clouds and text by matching parts from shapes to words from sentences in a common space.

Text Matching

Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Coding

1 code implementation CVPR 2021 Xin Wen, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu

We provide a comprehensive evaluation in experiments, which shows that our model with the learned bidirectional geometry correspondence outperforms state-of-the-art unpaired completion methods.

Point Cloud Completion

Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projections Matching

1 code implementation ICCV 2021 Chao Chen, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker

Our method pushes the neural network to generate a 3D point cloud whose 2D projections match the irregular point supervision from different view angles.

Point Cloud Generation

SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization

no code implementations8 Dec 2020 Xinhai Liu, Xinchen Liu, Zhizhong Han, Yu-Shen Liu

The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets.

PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths

1 code implementation CVPR 2021 Xin Wen, Peng Xiang, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu

As a result, the network learns a strict and unique correspondence on point-level, which can capture the detailed topology and structure relationships between the incomplete shape and the complete target, and thus improves the quality of the predicted complete shape.

Point Cloud Completion

Neural-Pull: Learning Signed Distance Functions from Point Clouds by Learning to Pull Space onto Surfaces

1 code implementation26 Nov 2020 Baorui Ma, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker

Specifically, we train a neural network to pull query 3D locations to their closest points on the surface using the predicted signed distance values and the gradient at the query locations, both of which are computed by the network itself.

Image Reconstruction Surface Reconstruction

DRWR: A Differentiable Renderer without Rendering for Unsupervised 3D Structure Learning from Silhouette Images

no code implementations ICML 2020 Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker

To optimize 3D shape parameters, current renderers rely on pixel-wise losses between rendered images of 3D reconstructions and ground truth images from corresponding viewpoints.

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

1 code implementation26 May 2020 Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker

According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category.

3D Shape Classification General Classification +2

Point Cloud Completion by Skip-attention Network with Hierarchical Folding

no code implementations CVPR 2020 Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu

Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones.

Point Cloud Completion

LRC-Net: Learning Discriminative Features on Point Clouds by Encoding Local Region Contexts

no code implementations18 Mar 2020 Xinhai Liu, Zhizhong Han, Fangzhou Hong, Yu-Shen Liu, Matthias Zwicker

However, due to the irregularity and sparsity in sampled point clouds, it is hard to encode the fine-grained geometry of local regions and their spatial relationships when only using the fixed-size filters and individual local feature integration, which limit the ability to learn discriminative features.

SeqXY2SeqZ: Structure Learning for 3D Shapes by Sequentially Predicting 1D Occupancy Segments From 2D Coordinates

no code implementations ECCV 2020 Zhizhong Han, Guanhui Qiao, Yu-Shen Liu, Matthias Zwicker

To avoid dense and irregular sampling in 3D, we propose to represent shapes using 2D functions, where the output of the function at each 2D location is a sequence of line segments inside the shape.

Learning to Generate Dense Point Clouds with Textures on Multiple Categories

1 code implementation22 Dec 2019 Tao Hu, Geng Lin, Zhizhong Han, Matthias Zwicker

In this paper, we propose a novel approach for reconstructing point clouds from RGB images.

3D Reconstruction

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

1 code implementation CVPR 2020 Yue Jiang, Dantong Ji, Zhizhong Han, Matthias Zwicker

We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs).

3D Reconstruction Single-View 3D Reconstruction

3D Shape Completion with Multi-view Consistent Inference

no code implementations28 Nov 2019 Tao Hu, Zhizhong Han, Matthias Zwicker

We formulate the regularization term as a consistency loss that encourages geometric consistency among multiple views, while the data term guarantees that the optimized views do not drift away too much from a learned shape descriptor.

Fast Low-rank Metric Learning for Large-scale and High-dimensional Data

1 code implementation NeurIPS 2019 Han Liu, Zhizhong Han, Yu-Shen Liu, Ming Gu

Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints.

Metric Learning

Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules

no code implementations29 Aug 2019 Xin Wen, Zhizhong Han, Xinhai Liu, Yu-Shen Liu

Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters.

L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention

no code implementations2 Aug 2019 Xinhai Liu, Zhizhong Han, Xin Wen, Yu-Shen Liu, Matthias Zwicker

Specifically, L2G-AE employs an encoder to encode the geometry information of multiple scales in a local region at the same time.

ShapeCaptioner: Generative Caption Network for 3D Shapes by Learning a Mapping from Parts Detected in Multiple Views to Sentences

no code implementations31 Jul 2019 Zhizhong Han, Chao Chen, Yu-Shen Liu, Matthias Zwicker

Specifically, ShapeCaptioner aggregates the parts detected in multiple colored views using our novel part class specific aggregation to represent a 3D shape, and then, employs a sequence to sequence model to generate the caption.

Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views

no code implementations18 May 2019 Zhizhong Han, Xinhai Liu, Yu-Shen Liu, Matthias Zwicker

In contrast, we propose a deep neural network, called Parts4Feature, to learn 3D global features from part-level information in multiple views.

Region Proposal

3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

no code implementations17 May 2019 Zhizhong Han, Xiyang Wang, Chi-Man Vong, Yu-Shen Liu, Matthias Zwicker, C. L. Philip Chen

Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns.

Adversarial Cross-Modal Retrieval via Learning and Transferring Single-Modal Similarities

no code implementations17 Apr 2019 Xin Wen, Zhizhong Han, Xinyu Yin, Yu-Shen Liu

Cross-modal retrieval aims to retrieve relevant data across different modalities (e. g., texts vs. images).

Cross-Modal Retrieval

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

no code implementations17 Apr 2019 Tao Hu, Zhizhong Han, Abhinav Shrivastava, Matthias Zwicker

Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view.

Image-to-Image Translation Translation

View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions

no code implementations7 Nov 2018 Zhizhong Han, Mingyang Shang, Yu-Shen Liu, Matthias Zwicker

Intuitively, this memory enables the system to aggregate information that is useful to better solve the view inter-prediction tasks for each shape, and to leverage the memory as a view-independent shape representation.

Representation Learning

Y^2Seq2Seq: Cross-Modal Representation Learning for 3D Shape and Text by Joint Reconstruction and Prediction of View and Word Sequences

no code implementations7 Nov 2018 Zhizhong Han, Mingyang Shang, Xiyang Wang, Yu-Shen Liu, Matthias Zwicker

A recent method employs 3D voxels to represent 3D shapes, but this limits the approach to low resolutions due to the computational cost caused by the cubic complexity of 3D voxels.

3D Shape Representation Cross-Modal Retrieval +1

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

no code implementations6 Nov 2018 Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker

However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features.

Ranked #32 on 3D Part Segmentation on ShapeNet-Part (Instance Average IoU metric)

3D Part Segmentation 3D Point Cloud Classification +1

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