2 citations · 2 across the 4 of their papers we have counts for
5 papers
Tri-Perspective View Decomposition for Geometry-Aware Depth Completion
Zhiqiang Yan, Yuankai Lin, Kun Wang +5
Depth completion is a vital task for autonomous driving, as it involves reconstructing the precise 3D geometry of a scene from sparse and noisy depth measurements. However, most ex…
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization
Kun Wang, Zhiqiang Yan, Huang Tian +4
Neural Radiance Fields (NeRF) have shown promise in generating realistic novel views from sparse scene images. However, existing NeRF approaches often encounter challenges due to t…
Variable Radiance Field for Real-World Category-Specific Reconstruction from Single Image
Kun Wang, Zhiqiang Yan, Zhenyu Zhang +3
Reconstructing category-specific objects using Neural Radiance Field (NeRF) from a single image is a promising yet challenging task. Existing approaches predominantly rely on proje…
DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion
Zhiqiang Yan, Kun Wang, Xiang Li +3
Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually s…
Multi-Modal Masked Pre-Training for Monocular Panoramic Depth Completion
Zhiqiang Yan, Xiang Li, Kun Wang +3
In this paper, we formulate a potentially valuable panoramic depth completion (PDC) task as panoramic 3D cameras often produce 360° depth with missing data in complex scenes. Its g…