74 citations · 540 across the 72 of their papers we have counts for
12 papers · 1 filter
Learning to Generate Dense Point Clouds with Textures on Multiple Categories
Tao Hu, Geng Lin, Zhizhong Han +1
3D reconstruction from images is a core problem in computer vision. With recent advances in deep learning, it has become possible to recover plausible 3D shapes even from single RG…
SDFDiff: Differentiable Rendering of Signed Distance Fields for 3D Shape Optimization
Yue Jiang, Dantong Ji, Zhizhong Han +1
We propose SDFDiff, a novel approach for image-based shape optimization using differentiable rendering of 3D shapes represented by signed distance functions (SDFs). Compared to oth…
3D Shape Completion with Multi-view Consistent Inference
Tao Hu, Zhizhong Han, Matthias Zwicker
3D shape completion is important to enable machines to perceive the complete geometry of objects from partial observations. To address this problem, view-based methods have been pr…
Fast Low-rank Metric Learning for Large-scale and High-dimensional Data
Han Liu, Zhizhong Han, Yu-Shen Liu +1
Low-rank metric learning aims to learn better discrimination of data subject to low-rank constraints. It keeps the intrinsic low-rank structure of datasets and reduces the time cos…
Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules
Xin Wen, Zhizhong Han, Xinhai Liu +1
Learning discriminative shape representation directly on point clouds is still challenging in 3D shape analysis and understanding. Recent studies usually involve three steps: first…
L2G Auto-encoder: Understanding Point Clouds by Local-to-Global Reconstruction with Hierarchical Self-Attention
Xinhai Liu, Zhizhong Han, Xin Wen +2
Auto-encoder is an important architecture to understand point clouds in an encoding and decoding procedure of self reconstruction. Current auto-encoder mainly focuses on the learni…
ShapeCaptioner: Generative Caption Network for 3D Shapes by Learning a Mapping from Parts Detected in Multiple Views to Sentences
Zhizhong Han, Chao Chen, Yu-Shen Liu +1
3D shape captioning is a challenging application in 3D shape understanding. Captions from recent multi-view based methods reveal that they cannot capture part-level characteristics…
Multi-Angle Point Cloud-VAE: Unsupervised Feature Learning for 3D Point Clouds from Multiple Angles by Joint Self-Reconstruction and Half-to-Half Prediction
Zhizhong Han, Xiyang Wang, Yu-Shen Liu +1
Unsupervised feature learning for point clouds has been vital for large-scale point cloud understanding. Recent deep learning based methods depend on learning global geometry from…
Parts4Feature: Learning 3D Global Features from Generally Semantic Parts in Multiple Views
Zhizhong Han, Xinhai Liu, Yu-Shen Liu +1
Deep learning has achieved remarkable results in 3D shape analysis by learning global shape features from the pixel-level over multiple views. Previous methods, however, compute lo…
3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention
Zhizhong Han, Xiyang Wang, Chi-Man Vong +3
Learning global features by aggregating information over multiple views has been shown to be effective for 3D shape analysis. For view aggregation in deep learning models, pooling…
Adversarial Cross-Modal Retrieval via Learning and Transferring Single-Modal Similarities
Xin Wen, Zhizhong Han, Xinyu Yin +1
Cross-modal retrieval aims to retrieve relevant data across different modalities (e.g., texts vs. images). The common strategy is to apply element-wise constraints between manually…
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion
Tao Hu, Zhizhong Han, Abhinav Shrivastava +1
We propose a novel approach for 3D shape completion by synthesizing multi-view depth maps. While previous work for shape completion relies on volumetric representations, meshes, or…