1 code implementation • 27 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.
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.
1 code implementation • 18 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.
1 code implementation • 13 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.
1 code implementation • 11 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.
1 code implementation • 12 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.
1 code implementation • 7 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.
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.
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.
Ranked #4 on 6D Pose Estimation using RGBD on REAL275
1 code implementation • 10 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.
2 code implementations • 24 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 ($GeoA^3$).
no code implementations • 25 Sep 2019 • Yuxin Wen, Jiehong Lin, Ke Chen, Kui Jia
Recent studies show that machine learning models are vulnerable to adversarial examples.
no code implementations • 25 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.