22 citations · 36 across the 12 of their papers we have counts for
12 papers
ViP-Rig: Visual-Prompted Controllable Rigging
Zihan Qin, Mingze Sun, Yifan Mao +6
Rigging is inherently task-dependent because the same mesh may require different skeletons and deformation behaviors across animation tasks. In practice, artists often inspect an i…
The Fourth Monocular Depth Estimation Challenge
Anton Obukhov, Matteo Poggi, Fabio Tosi +54
This paper presents the results of the fourth edition of the Monocular Depth Estimation Challenge (MDEC), which focuses on zero-shot generalization to the SYNS-Patches benchmark, a…
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
Zihan Qin, Jialei Xu, Wenbo Zhao +2
Depth estimation under adverse conditions remains a significant challenge. Recently, multi-spectral depth estimation, which integrates both visible light and thermal images, has sh…
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +88
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles. Challenges such as adverse weather, sen…
DINO-SD: Champion Solution for ICRA 2024 RoboDepth Challenge
Yifan Mao, Ming Li, Jian Liu +7
Surround-view depth estimation is a crucial task aims to acquire the depth maps of the surrounding views. It has many applications in real world scenarios such as autonomous drivin…
SDGE: Stereo Guided Depth Estimation for 360 Camera Sets
Jialei Xu, Wei Yin, Dong Gong +2
Depth estimation is a critical technology in autonomous driving, and multi-camera systems are often used to achieve a 360 perception. These 360 camera sets often ha…
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Jialei Xu, Xianming Liu, Junjun Jiang +4
Monocular depth estimation from RGB images plays a pivotal role in 3D vision. However, its accuracy can deteriorate in challenging environments such as nighttime or adverse weather…
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
Andrey Ignatov, Grigory Malivenko, Radu Timofte +36
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
Jialei Xu, Xianming Liu, Yuanchao Bai +4
Depth map estimation from images is an important task in robotic systems. Existing methods can be categorized into two groups including multi-view stereo and monocular depth estima…
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices
Zhenyu Li, Zehui Chen, Jialei Xu +2
Monocular depth estimation is an essential task in the computer vision community. While tremendous successful methods have obtained excellent results, most of them are computationa…
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data
Jialei Xu, Yuanchao Bai, Xianming Liu +2
Depth estimation from a single image is an active research topic in computer vision. The most accurate approaches are based on fully supervised learning models, which rely on a lar…
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Grigory Malivenko, David Plowman +35
Depth estimation is an important computer vision problem with many practical applications to mobile devices. While many solutions have been proposed for this task, they are usually…