37 citations · 84 across the 13 of their papers we have counts for
14 papers
FoundationGeo: Learning Spatial Pixel-Wise Fields for Monocular Metric Geometry
Muxin Liu, Xiaoyang Lyu, Tianhe Ren +7
We present FoundationGeo, a two-stage framework that explicitly bridges relative and metric prediction via spatial calibration and principled data design. Stage 1 learns a high-fid…
PicoPose: Progressive Pixel-to-Pixel Correspondence Learning for Novel Object Pose Estimation
Lihua Liu, Jiehong Lin, Zhenxin Liu +1
RGB-based novel object pose estimation is critical for rapid deployment in robotic applications, yet zero-shot generalization remains a key challenge. In this paper, we introduce P…
SAM-6D: Segment Anything Model Meets Zero-Shot 6D Object Pose Estimation
Jiehong Lin, Lihua Liu, Dekun Lu +1
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. F…
VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations
Jiehong Lin, Zewei Wei, Yabin Zhang +1
Rotation estimation of high precision from an RGB-D object observation is a huge challenge in 6D object pose estimation, due to the difficulty of learning in the non-linear space o…
Manifold-Aware Self-Training for Unsupervised Domain Adaptation on Regressing 6D Object Pose
Yichen Zhang, Jiehong Lin, Ke Chen +3
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 c…
Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning
Yabin Zhang, Jiehong Lin, Ruihuang Li +2
Masked autoencoder has demonstrated its effectiveness in self-supervised point cloud learning. Considering that masking is a kind of corruption, in this work we explore a more gene…
DCL-Net: Deep Correspondence Learning Network for 6D Pose Estimation
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. However, surrogate objectives of correspondence lear…
Category-Level 6D Object Pose and Size Estimation using Self-Supervised Deep Prior Deformation Networks
Jiehong Lin, Zewei Wei, Changxing Ding +1
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 ob…
Masked Surfel Prediction for Self-Supervised Point Cloud Learning
Yabin Zhang, Jiehong Lin, Chenhang He +3
Masked auto-encoding is a popular and effective self-supervised learning approach to point cloud learning. However, most of the existing methods reconstruct only the masked points…
Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
Jiehong Lin, Hongyang Li, Ke Chen +2
As a basic component of SE(3)-equivariant deep feature learning, steerable convolution has recently demonstrated its advantages for 3D semantic analysis. The advantages are, howeve…
DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency
Jiehong Lin, Zewei Wei, Zhihao Li +3
Category-level 6D object pose and size estimation is to predict full pose configurations of rotation, translation, and size for object instances observed in single, arbitrary views…
CAD-PU: A Curvature-Adaptive Deep Learning Solution for Point Set Upsampling
Jiehong Lin, Xian Shi, Yuan Gao +2
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 poss…
Geometry-Aware Generation of Adversarial Point Clouds
Yuxin Wen, Jiehong Lin, Ke Chen +2
Machine learning models have been shown to be vulnerable to adversarial examples. While most of the existing methods for adversarial attack and defense work on the 2D image domain,…
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers
Kui Jia, Jiehong Lin, Mingkui Tan +1
Many machine learning problems concern with discovering or associating common patterns in data of multiple views or modalities. Multi-view learning is of the methods to achieve suc…