activity
20222024
most citedFDCT: Fast Depth Completion for Transparent Objects

33 citations · 58 across the 8 of their papers we have counts for

collaborators
Showing cs.CVShow all

6 papers · 1 filter

cs.CV202416 cited

Salient Sparse Visual Odometry With Pose-Only Supervision

Siyu Chen, Kangcheng Liu, Chen Wang +3

Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs. While traditional VO methods exce…

cs.CV202333 cited

FDCT: Fast Depth Completion for Transparent Objects

Tianan Li, Zhehan Chen, Huan Liu +1

Depth completion is crucial for many robotic tasks such as autonomous driving, 3-D reconstruction, and manipulation. Despite the significant progress, existing methods remain compu…

cs.CV20231 cited

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

Yiqing Wang, Zihan Li, Jieru Mei +7

Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a n…

cs.CV2023

AirLoc: Object-based Indoor Relocalization

Aryan, Bowen Li, Sebastian Scherer +2

Indoor relocalization is vital for both robotic tasks like autonomous exploration and civil applications such as navigation with a cell phone in a shopping mall. Some previous appr…

cs.CV20232 cited

HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation

Linfang Zheng, Chen Wang, Yinghan Sun +5

In this paper, we focus on the problem of category-level object pose estimation, which is challenging due to the large intra-category shape variation. 3D graph convolution (3D-GC)…

cs.CV2022

GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning

Huseyin Coskun, Alireza Zareian, Joshua L. Moore +2

Clustering is a ubiquitous tool in unsupervised learning. Most of the existing self-supervised representation learning methods typically cluster samples based on visually dominant…