9 citations · 11 across the 8 of their papers we have counts for
8 papers
AnyImageNav: Any-View Geometry for Precise Last-Meter Image-Goal Navigation
Yijie Deng, Shuaihang Yuan, Yi Fang
Image Goal Navigation (ImageNav) is evaluated by a coarse success criterion, the agent must stop within 1m of the target, which is sufficient for finding objects but falls short fo…
GaussianGrow: Geometry-aware Gaussian Growing from 3D Point Clouds with Text Guidance
Weiqi Zhang, Junsheng Zhou, Haotian Geng +4
3D Gaussian Splatting has demonstrated superior performance in rendering efficiency and quality, yet the generation of 3D Gaussians still remains a challenge without proper geometr…
PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +4
Estimating the normal of a point requires constructing a local patch to provide center-surrounding context, but determining the appropriate neighborhood size is difficult when deal…
U-CAN: Unsupervised Point Cloud Denoising with Consistency-Aware Noise2Noise Matching
Junsheng Zhou, Xingyu Shi, Haichuan Song +3
Point clouds captured by scanning sensors are often perturbed by noise, which have a highly negative impact on downstream tasks (e.g. surface reconstruction and shape understanding…
MaterialRefGS: Reflective Gaussian Splatting with Multi-view Consistent Material Inference
Wenyuan Zhang, Jimin Tang, Weiqi Zhang +3
Modeling reflections from 2D images is essential for photorealistic rendering and novel view synthesis. Recent approaches enhance Gaussian primitives with reflection-related materi…
Differentiable Registration of Images and LiDAR Point Clouds with VoxelPoint-to-Pixel Matching
Junsheng Zhou, Baorui Ma, Wenyuan Zhang +3
Cross-modality registration between 2D images from cameras and 3D point clouds from LiDARs is a crucial task in computer vision and robotic. Previous methods estimate 2D-3D corresp…
NeuralGF: Unsupervised Point Normal Estimation by Learning Neural Gradient Function
Qing Li, Huifang Feng, Kanle Shi +4
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal sup…
Learning Signed Hyper Surfaces for Oriented Point Cloud Normal Estimation
Qing Li, Huifang Feng, Kanle Shi +4
We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consiste…