9 citations · 10 across the 4 of their papers we have counts for
4 papers
Diffusion-Augmented Depth Prediction with Sparse Annotations
Jiaqi Li, Yiran Wang, Zihao Huang +4
Depth estimation aims to predict dense depth maps. In autonomous driving scenes, sparsity of annotations makes the task challenging. Supervised models produce concave objects due t…
Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells
Xinyi Ye, Weiyue Zhao, Tianqi Liu +3
Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation. Despite the excellent performance, ex…
Rethinking the Misalignment Problem in Dense Object Detection
Yang Yang, Min Li, Bo Meng +3
Object detection aims to localize and classify the objects in a given image, and these two tasks are sensitive to different object regions. Therefore, some locations predict high-q…
Objects as Extreme Points
Yang Yang, Min Li, Bo Meng +3
Object detection can be regarded as a pixel clustering task, and its boundary is determined by four extreme points (leftmost, top, rightmost, and bottom). However, most studies foc…