Search Results for author: Jiehong Lin

Found 13 papers, 11 papers with code

SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation

1 code implementation27 Nov 2023 Jiehong Lin, Lihua Liu, Dekun Lu, Kui Jia

Zero-shot 6D object pose estimation involves the detection of novel objects with their 6D poses in cluttered scenes, presenting significant challenges for model generalizability.

6D Pose Estimation using RGB Instance Segmentation +4

VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations

1 code implementation ICCV 2023 Jiehong Lin, Zewei Wei, Yabin Zhang, Kui Jia

We apply the proposed VI-Net to the challenging task of category-level 6D object pose estimation for predicting the poses of unknown objects without available CAD models; experiments on the benchmarking datasets confirm the efficacy of our method, which outperforms the existing ones with a large margin in the regime of high precision.

6D Pose Estimation using RGB Benchmarking +2

Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose

1 code implementation18 May 2023 Yichen Zhang, Jiehong Lin, Ke Chen, Zelin Xu, YaoWei Wang, Kui Jia

Domain gap between synthetic and real data in visual regression (e. g. 6D pose estimation) is bridged in this paper via global feature alignment and local refinement on the coarse classification of discretized anchor classes in target space, which imposes a piece-wise target manifold regularization into domain-invariant representation learning.

6D Pose Estimation regression +2

Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning

1 code implementation13 Nov 2022 Yabin Zhang, Jiehong Lin, Ruihuang Li, Kui Jia, Lei Zhang

We also validate the effectiveness of affine transformation corruption with the Transformer backbones, where we decompose the reconstruction of the complete point cloud into the reconstructions of detailed local patches and rough global shape, alleviating the position leakage problem in the reconstruction.

3D Object Detection Denoising +2

DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation

1 code implementation11 Oct 2022 Hongyang Li, Jiehong Lin, Kui Jia

Establishment of point correspondence between camera and object coordinate systems is a promising way to solve 6D object poses.

6D Pose Estimation 6D Pose Estimation using RGB +2

Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks

1 code implementation12 Jul 2022 Jiehong Lin, Zewei Wei, Changxing Ding, Kui Jia

It is difficult to precisely annotate object instances and their semantics in 3D space, and as such, synthetic data are extensively used for these tasks, e. g., category-level 6D object pose and size estimation.

6D Pose Estimation Object +1

Masked Surfel Prediction for Self-Supervised Point Cloud Learning

1 code implementation7 Jul 2022 Yabin Zhang, Jiehong Lin, Chenhang He, Yongwei Chen, Kui Jia, Lei Zhang

In this work, we make the first attempt, to the best of our knowledge, to consider the local geometry information explicitly into the masked auto-encoding, and propose a novel Masked Surfel Prediction (MaskSurf) method.

Point cloud reconstruction Self-Supervised Learning

Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space

1 code implementation NeurIPS 2021 Jiehong Lin, Hongyang Li, Ke Chen, Jiangbo Lu, Kui Jia

In this paper, we propose a novel design of Sparse Steerable Convolution (SS-Conv) to address the shortcoming; SS-Conv greatly accelerates steerable convolution with sparse tensors, while strictly preserving the property of SE(3)-equivariance.

6D Pose Estimation Pose Tracking

DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency

1 code implementation ICCV 2021 Jiehong Lin, Zewei Wei, Zhihao LI, Songcen Xu, Kui Jia, Yuanqing Li

DualPoseNet stacks two parallel pose decoders on top of a shared pose encoder, where the implicit decoder predicts object poses with a working mechanism different from that of the explicit one; they thus impose complementary supervision on the training of pose encoder.

6D Pose Estimation using RGBD Object +1

CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling

1 code implementation10 Sep 2020 Jiehong Lin, Xian Shi, Yuan Gao, Ke Chen, Kui Jia

Point set is arguably the most direct approximation of an object or scene surface, yet its practical acquisition often suffers from the shortcoming of being noisy, sparse, and possibly incomplete, which restricts its use for a high-quality surface recovery.

Point Set Upsampling

Geometry-Aware Generation of Adversarial Point Clouds

2 code implementations24 Dec 2019 Yuxin Wen, Jiehong Lin, Ke Chen, C. L. Philip Chen, Kui Jia

Regularizing the targeted attack loss with our proposed geometry-aware objectives results in our proposed method, Geometry-Aware Adversarial Attack ($GeoA^3$).

Adversarial Attack Fairness

Geometry-aware Generation of Adversarial and Cooperative Point Clouds

no code implementations25 Sep 2019 Yuxin Wen, Jiehong Lin, Ke Chen, Kui Jia

Recent studies show that machine learning models are vulnerable to adversarial examples.

Fairness Object

Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers

no code implementations25 Apr 2019 Kui Jia, Jiehong Lin, Mingkui Tan, DaCheng Tao

Such a perspective enables us to study deep multi-view learning in the context of regularized network training, for which we present control experiments of benchmark image classification to show the efficacy of our proposed CorrReg.

3D Object Recognition General Classification +3

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