260 citations · 298 across the 9 of their papers we have counts for
9 papers · 1 filter
How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
Fabio Tosi, Youmin Zhang, Ziren Gong +4
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling a…
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
Youmin Zhang, Fabio Tosi, Stefano Mattoccia +1
Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in ca…
CompletionFormer: Depth Completion with Convolutions and Vision Transformers
Zhang Youmin, Guo Xianda, Poggi Matteo +3
Given sparse depths and the corresponding RGB images, depth completion aims at spatially propagating the sparse measurements throughout the whole image to get a dense depth predict…
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
Xin Qiao, Chenyang Ge, Youmin Zhang +4
We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features. The f…
The Monocular Depth Estimation Challenge
Jaime Spencer, C. Stella Qian, Chris Russell +16
This paper summarizes the results of the first Monocular Depth Estimation Challenge (MDEC) organized at WACV2023. This challenge evaluated the progress of self-supervised monocular…
TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network
Youmin Zhang, Matteo Poggi, Stefano Mattoccia
We present TemporalStereo, a coarse-to-fine stereo matching network that is highly efficient, and able to effectively exploit the past geometry and context information to boost mat…
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
Chaoqiang Zhao, Youmin Zhang, Matteo Poggi +6
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth labels for training. Convolutional neural networks (CNNs) have recen…
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
Suihanjin Yu, Youmin Zhang, Chen Wang +3
In optical flow estimation task, coarse-to-fine (C2F) warping strategy is widely used to deal with the large displacement problem and provides efficiency and speed. However, limite…
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
Youmin Zhang, Yimin Chen, Xiao Bai +4
State-of-the-art deep learning based stereo matching approaches treat disparity estimation as a regression problem, where loss function is directly defined on true disparities and…