Search Results for author:
Found 8 papers, 3 papers with code
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
This paper enables high-fidelity, transferable NeRF editing by frequency decomposition.
OV9D: Open-Vocabulary Category-Level 9D Object Pose and Size Estimation
Derived from OmniObject3D, OO3D-9D is the largest and most diverse dataset in the field of category-level object pose and size estimation.
Sketch2NeRF: Multi-view Sketch-guided Text-to-3D Generation
We propose a novel synchronized generation and reconstruction method to effectively optimize the NeRF.
FS6D: Few-Shot 6D Pose Estimation of Novel Objects
6D object pose estimation networks are limited in their capability to scale to large numbers of object instances due to the close-set assumption and their reliance on high-fidelity object CAD models.
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Instead, we propose a label-free method that learns to enforce the geometric consistency between category template mesh and observed object point cloud under a self-supervision manner.
iShape: A First Step Towards Irregular Shape Instance Segmentation
In this paper, we introduce a brand new dataset to promote the study of instance segmentation for objects with irregular shapes.
Ranked #1 on
Instance Segmentation
on iShape
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
Moreover, at the output representation stage, we designed a simple but effective 3D keypoints selection algorithm considering the texture and geometry information of objects, which simplifies keypoint localization for precise pose estimation.
Ranked #1 on
6D Pose Estimation
on LineMOD
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation.
Ranked #1 on
6D Pose Estimation using RGBD
on YCB-Video
(Mean ADD-S metric)
