Search Results for author: Le Hui

Found 17 papers, 12 papers with code

Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking

no code implementations14 Sep 2022 Haobo Jiang, Kaihao Lan, Le Hui, Guangyu Li, Jin Xie, Jian Yang

The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization.

Object Localization Object Tracking +1

Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching

no code implementations9 Aug 2022 Yikai Bian, Le Hui, Jianjun Qian, Jin Xie

Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data.

Graph Matching Semantic Segmentation +1

RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation

1 code implementation25 Jul 2022 Mu He, Le Hui, Yikai Bian, Jian Ren, Jin Xie, Jian Yang

In this paper, we propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth.

Data Augmentation Monocular Depth Estimation

3D Siamese Transformer Network for Single Object Tracking on Point Clouds

1 code implementation25 Jul 2022 Le Hui, Lingpeng Wang, Linghua Tang, Kaihao Lan, Jin Xie, Jian Yang

Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area.

Object Tracking

Generative Subgraph Contrast for Self-Supervised Graph Representation Learning

1 code implementation25 Jul 2022 Yuehui Han, Le Hui, Haobo Jiang, Jianjun Qian, Jin Xie

To this end, in this paper, we propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning, and the optimal transport distance is utilized as the similarity metric between the subgraphs.

Contrastive Learning Graph Representation Learning +1

Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval

no code implementations24 Feb 2022 Rui Xu, Zongyan Han, Le Hui, Jianjun Qian, Jin Xie

Then, we develop a generative adversarial network that combines the domain-specific features of the seen categories with the aligned domain-invariant features to synthesize samples, where the synthesized samples of the unseen categories are generated by using the corresponding word embeddings.

3D Shape Retrieval Word Embeddings

Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

1 code implementation23 Feb 2022 Yaqi Shen, Le Hui, Haobo Jiang, Jin Xie, Jian Yang

In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration.

Model Optimization Point Cloud Registration

3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds

1 code implementation NeurIPS 2021 Le Hui, Lingpeng Wang, Mingmei Cheng, Jin Xie, Jian Yang

The Siamese shape-aware feature learning network can capture 3D shape information of the object to learn the discriminative features of the object so that the potential target from the background in sparse point clouds can be identified.

3D Object Tracking Object Tracking

SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering

1 code implementation1 Aug 2021 Yifan Zhao, Le Hui, Jin Xie

To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images.

SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network

1 code implementation16 Apr 2021 Mingmei Cheng, Le Hui, Jin Xie, Jian Yang

In order to reduce the number of annotated labels, we propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net, where we train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points.

Point Cloud Segmentation Scene Understanding +1

Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition

1 code implementation7 Jan 2021 Le Hui, Mingmei Cheng, Jin Xie, Jian Yang

In this paper, we develop an efficient point cloud learning network (EPC-Net) to form a global descriptor for visual place recognition, which can obtain good performance and reduce computation memory and inference time.

Point Cloud Retrieval Visual Place Recognition

Superpoint Network for Point Cloud Oversegmentation

1 code implementation ICCV 2021 Le Hui, Jia Yuan, Mingmei Cheng, Jin Xie, Xiaoya Zhang, Jian Yang

Specifically, in our clustering network, we first jointly learn a soft point-superpoint association map from the coordinate and feature spaces of point clouds, where each point is assigned to the superpoint with a learned weight.

Semantic Segmentation

Pyramid Point Cloud Transformer for Large-Scale Place Recognition

1 code implementation ICCV 2021 Le Hui, Hang Yang, Mingmei Cheng, Jin Xie, Jian Yang

In order to obtain discriminative global descriptors, we construct a pyramid VLAD module to aggregate the multi-scale feature maps of point clouds into the global descriptors.

Point Cloud Retrieval

Progressive Point Cloud Deconvolution Generation Network

1 code implementation ECCV 2020 Le Hui, Rui Xu, Jin Xie, Jianjun Qian, Jian Yang

Starting from the low-resolution point clouds, with the bilateral interpolation and max-pooling operations, the deconvolution network can progressively output high-resolution local and global feature maps.

Point Cloud Generation

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