8 citations · 10 across the 6 of their papers we have counts for
6 papers
EPS3D: End-to-End Feed-Forward 3D Panoptic Segmentation
Runsong Zhu, Jiaxin Guo, Xiaoyang Guo +9
This paper introduces EPS3D, a new end-to-end feed-forward framework for open-vocabulary 3D panoptic segmentation. Unlike existing methods relying on additional preprocessing, we d…
COS3D: Collaborative Open-Vocabulary 3D Segmentation
Runsong Zhu, Ka-Hei Hui, Zhengzhe Liu +5
Open-vocabulary 3D segmentation is a fundamental yet challenging task, requiring a mutual understanding of both segmentation and language. However, existing Gaussian-splatting-base…
Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting
Runsong Zhu, Shi Qiu, Zhengzhe Liu +4
Lifting multi-view 2D instance segmentation to a radiance field has proven to be effective to enhance 3D understanding. Existing methods rely on direct matching for end-to-end lift…
PCF-Lift: Panoptic Lifting by Probabilistic Contrastive Fusion
Runsong Zhu, Shi Qiu, Qianyi Wu +3
Panoptic lifting is an effective technique to address the 3D panoptic segmentation task by unprojecting 2D panoptic segmentations from multi-views to 3D scene. However, the quality…
Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds
Runsong Zhu, Di Kang, Ka-Hei Hui +6
Reconstructing 3D geometry from \emph{unoriented} point clouds can benefit many downstream tasks. Recent shape modeling methods mostly adopt implicit neural representation to fit a…
AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
Runsong Zhu, Yuan Liu, Zhen Dong +4
This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations. Existing works u…