1 citations · 1 across the 18 of their papers we have counts for
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Street Gaussians without 3D Object Tracker
Ruida Zhang, Chengxi Li, Chenyangguang Zhang +5
Realistic scene reconstruction in driving scenarios poses significant challenges due to fast-moving objects. Most existing methods rely on labor-intensive manual labeling of object…
GFreeDet: Exploiting Gaussian Splatting and Foundation Models for Model-free Unseen Object Detection in the BOP Challenge 2024
Xingyu Liu, Gu Wang, Chengxi Li +4
We present GFreeDet, an unseen object detection approach that leverages Gaussian splatting and vision Foundation models under model-free setting. Unlike existing methods that rely…
UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image
Xingyu Liu, Gu Wang, Ruida Zhang +3
Unseen object pose estimation methods often rely on CAD models or multiple reference views, making the onboarding stage costly. To simplify reference acquisition, we aim to estimat…
UW-SDF: Exploiting Hybrid Geometric Priors for Neural SDF Reconstruction from Underwater Multi-view Monocular Images
Zeyu Chen, Jingyi Tang, Gu Wang +4
Due to the unique characteristics of underwater environments, accurate 3D reconstruction of underwater objects poses a challenging problem in tasks such as underwater exploration a…
FAFA: Frequency-Aware Flow-Aided Self-Supervision for Underwater Object Pose Estimation
Jingyi Tang, Gu Wang, Zeyu Chen +3
Although methods for estimating the pose of objects in indoor scenes have achieved great success, the pose estimation of underwater objects remains challenging due to difficulties…
LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation
Ruida Zhang, Ziqin Huang, Gu Wang +5
While RGBD-based methods for category-level object pose estimation hold promise, their reliance on depth data limits their applicability in diverse scenarios. In response, recent e…
RaSim: A Range-aware High-fidelity RGB-D Data Simulation Pipeline for Real-world Applications
Xingyu Liu, Chenyangguang Zhang, Gu Wang +2
In robotic vision, a de-facto paradigm is to learn in simulated environments and then transfer to real-world applications, which poses an essential challenge in bridging the sim-to…
KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation
Ruida Zhang, Chenyangguang Zhang, Yan Di +4
In this paper, we present KP-RED, a unified KeyPoint-driven REtrieval and Deformation framework that takes object scans as input and jointly retrieves and deforms the most geometri…