95 citations · 111 across the 9 of their papers we have counts for
9 papers
From Uncertainty to Stability and Fidelity: Guiding Sparse-View 3D Gaussian Splatting with Fisher Information
Junbao Zhou, Qingshan Xu, Yuan Zhou +7
3D Gaussian Splatting (3DGS) has emerged as a promising technique for novel view synthesis. However, 3DGS requires dense input views to achieve high-quality rendering. In sparse-vi…
DrivePTS: A Progressive Learning Framework with Textual and Structural Enhancement for Driving Scene Generation
Zhechao Wang, Yiming Zeng, Lufan Ma +4
Synthesis of diverse driving scenes serves as a crucial data augmentation technique for validating the robustness and generalizability of autonomous driving systems. Current method…
Dynamic 3D Point Cloud Sequences as 2D Videos
Yiming Zeng, Junhui Hou, Qijian Zhang +2
Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in b…
Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis
Qijian Zhang, Junhui Hou, Yue Qian +3
Point clouds are characterized by irregularity and unstructuredness, which pose challenges in efficient data exploitation and discriminative feature extraction. In this paper, we p…
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
Siyu Ren, Yiming Zeng, Junhui Hou +1
Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the fir…
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation
Yingzhi Tang, Yue Qian, Qijian Zhang +3
We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions b…
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
Yiming Zeng, Yue Qian, Qijian Zhang +3
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise traje…
CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds
Yiming Zeng, Yue Qian, Zhiyu Zhu +3
Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -- the first unsu…
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling
Yue Qian, Junhui Hou, Qijian Zhang +3
This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applicati…