81 citations · 190 across the 13 of their papers we have counts for
13 papers
Look Up and Look Back: Hidden Attention and Latent Orientation in a Frozen Foundation Model for Panoramic SLAM
Zhuang Xiong, Guohao Zhang, Chen Zhang +4
Monocular panoramic SLAM benefits from substantial visual overlap under large camera rotations, yet remains prone to errors caused by camera tilt, scale drift, and false loop closu…
VGGT-Motion: Motion-Aware Calibration-Free Monocular SLAM for Long-Range Consistency
Zhuang Xiong, Chen Zhang, Qingshan Xu +1
Despite recent progress in calibration-free monocular SLAM via 3D vision foundation models, scale drift remains severe on long sequences. Motion-agnostic partitioning breaks contex…
PSDF: Prior-Driven Neural Implicit Surface Learning for Multi-view Reconstruction
Wanjuan Su, Chen Zhang, Qingshan Xu +1
Surface reconstruction has traditionally relied on the Multi-View Stereo (MVS)-based pipeline, which often suffers from noisy and incomplete geometry. This is due to that although…
PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction
Jianyao Xu, Qingshan Xu, Xinyao Liao +4
Neural surfaces learning has shown impressive performance in multi-view surface reconstruction. However, most existing methods use large multilayer perceptrons (MLPs) to train thei…
PG-NeuS: Robust and Efficient Point Guidance for Multi-View Neural Surface Reconstruction
Chen Zhang, Wanjuan Su, Qingshan Xu +1
Recently, learning multi-view neural surface reconstruction with the supervision of point clouds or depth maps has been a promising way. However, due to the underutilization of pri…
Hierarchical Prior Mining for Non-local Multi-View Stereo
Chunlin Ren, Qingshan Xu, Shikun Zhang +1
As a fundamental problem in computer vision, multi-view stereo (MVS) aims at recovering the 3D geometry of a target from a set of 2D images. Recent advances in MVS have shown that…
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
Qiancheng Fu, Qingshan Xu, Yew-Soon Ong +1
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction. However, one key challenge remains: existing approaches lack expli…
Non-local Recurrent Regularization Networks for Multi-view Stereo
Qingshan Xu, Martin R. Oswald, Wenbing Tao +2
In deep multi-view stereo networks, cost regularization is crucial to achieve accurate depth estimation. Since 3D cost volume filtering is usually memory-consuming, recurrent 2D co…
PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network
Qingshan Xu, Wenbing Tao
Recently, learning-based multi-view stereo methods have achieved promising results. However, they all overlook the visibility difference among different views, which leads to an in…
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume
Qingshan Xu, Wenbing Tao
Deep learning has shown to be effective for depth inference in multi-view stereo (MVS). However, the scalability and accuracy still remain an open problem in this domain. This can…
Planar Prior Assisted PatchMatch Multi-View Stereo
Qingshan Xu, Wenbing Tao
The completeness of 3D models is still a challenging problem in multi-view stereo (MVS) due to the unreliable photometric consistency in low-textured areas. Since low-textured area…
Multi-Scale Geometric Consistency Guided Multi-View Stereo
Qingshan Xu, Wenbing Tao
In this paper, we propose an efficient multi-scale geometric consistency guided multi-view stereo method for accurate and complete depth map estimation. We first present our basic…
Multi-View Stereo with Asymmetric Checkerboard Propagation and Multi-Hypothesis Joint View Selection
Qingshan Xu, Wenbing Tao
In computer vision domain, how to fast and accurately perform multiview stereo (MVS) is still a challenging problem. In this paper we present a fast yet accurate method for 3D dens…