← Search

Yuankai Lin

6 accepted papers

2025

3D Vision-tactile Reconstruction from Infrared and Visible Images for Robotic Fine-grained Tactile Perception

IROS 2025

To achieve human-like haptic perception in anthropomorphic grippers, the compliant sensing surfaces of vision tactile sensor (VTS) must evolve from conventional planar configurations to biomimetically curved topographies with continuous surface gradients. However, planar VTSs have challenges when ex

Cited by 1SourceScholar
2025

MTIL: Encoding Full History With Mamba for Temporal Imitation Learning

RA-L 2025

Standard imitation learning (IL) methods have achieved considerable success in robotics, yet often rely on the Markov assumption, which falters in long-horizon tasks where history is crucial for resolving perceptual ambiguity. This limitation stems not only from a conceptual gap but also from a fund

Cited by 6SourcecodeScholar
2024

Tri-Perspective View Decomposition for Geometry-Aware Depth Completion

CVPR 2024poster

Depth completion is a vital task for autonomous driving as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However most existing methods either rely only on 2D depth representations or directly incorporate raw 3D point clouds for compensation w…

Cited by 30SourcePDFScholar
2023

BEV@DC: Bird's-Eye View Assisted Training for Depth Completion

CVPR 2023poster

Depth completion plays a crucial role in autonomous driving, in which cameras and LiDARs are two complementary sensors. Recent approaches attempt to exploit spatial geometric constraints hidden in LiDARs to enhance image-guided depth completion. However, only low efficiency and poor generalization c…

Cited by 30SourcePDFScholar
2022

Dynamic Spatial Propagation Network for Depth Completion

AAAI 2022technical

Image-guided depth completion aims to generate dense depth maps with sparse depth measurements and corresponding RGB images. Currently, spatial propagation networks (SPNs) are the most popular affinity-based methods in depth completion, but they still suffer from the representation limitation of the…

Cited by 137SourcePDFScholar