33 citations · 117 across the 14 of their papers we have counts for
17 papers
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Xingxing Zuo, Nan Yang, Nathaniel Merrill +2
Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications. Feature volumes have recently b…
Random Padding Data Augmentation
Nan Yang, Laicheng Zhong, Fan Huang +2
The convolutional neural network (CNN) learns the same object in different positions in images, which can improve the recognition accuracy of the model. An implication of this is t…
Behind the Scenes: Density Fields for Single View Reconstruction
Felix Wimbauer, Nan Yang, Christian Rupprecht +1
Inferring a meaningful geometric scene representation from a single image is a fundamental problem in computer vision. Approaches based on traditional depth map prediction can only…
4Seasons: Benchmarking Visual SLAM and Long-Term Localization for Autonomous Driving in Challenging Conditions
Patrick Wenzel, Nan Yang, Rui Wang +2
In this paper, we present a novel visual SLAM and long-term localization benchmark for autonomous driving in challenging conditions based on the large-scale 4Seasons dataset. The p…
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions
Tarun Yenamandra, Ayush Tewari, Nan Yang +3
Neural 3D implicit representations learn priors that are useful for diverse applications, such as single- or multiple-view 3D reconstruction. A major downside of existing approache…
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo
Lukas Koestler, Nan Yang, Niclas Zeller +1
In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding w…
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera
Felix Wimbauer, Nan Yang, Lukas von Stumberg +2
In this paper, we propose MonoRec, a semi-supervised monocular dense reconstruction architecture that predicts depth maps from a single moving camera in dynamic environments. MonoR…
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization
Lukas von Stumberg, Patrick Wenzel, Nan Yang +1
We present LM-Reloc -- a novel approach for visual relocalization based on direct image alignment. In contrast to prior works that tackle the problem with a feature-based formulati…
Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels
L. Koestler, N. Yang, R. Wang +1
The training of deep-learning-based 3D object detectors requires large datasets with 3D bounding box labels for supervision that have to be generated by hand-labeling. We propose a…
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
Patrick Wenzel, Rui Wang, Nan Yang +5
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving. Among others, it enables research on visual odometry, global place recogn…
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
Nan Yang, Lukas von Stumberg, Rui Wang +1
We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation. We first propose a no…
Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light
Eunah Jung, Nan Yang, Daniel Cremers
We propose the concept of a multi-frame GAN (MFGAN) and demonstrate its potential as an image sequence enhancement for stereo visual odometry in low light conditions. We base our m…
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
Rui Wang, Nan Yang, Joerg Stueckler +1
Scene understanding from images is a challenging problem encountered in autonomous driving. On the object level, while 2D methods have gradually evolved from computing simple bound…
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry
Nan Yang, Rui Wang, Jörg Stückler +1
Monocular visual odometry approaches that purely rely on geometric cues are prone to scale drift and require sufficient motion parallax in successive frames for motion estimation a…
Ambiguity set and learning via Bregman and Wasserstein
Xin Guo, Johnny Hong, Nan Yang
Construction of ambiguity set in robust optimization relies on the choice of divergences between probability distributions. In distribution learning, choosing appropriate probabili…
Relaxed Wasserstein with Applications to GANs
Xin Guo, Johnny Hong, Tianyi Lin +1
Wasserstein Generative Adversarial Networks (WGANs) provide a versatile class of models, which have attracted great attention in various applications. However, this framework has t…
Challenges in Monocular Visual Odometry: Photometric Calibration, Motion Bias and Rolling Shutter Effect
Nan Yang, Rui Wang, Xiang Gao +1
Monocular visual odometry (VO) and simultaneous localization and mapping (SLAM) have seen tremendous improvements in accuracy, robustness and efficiency, and have gained increasing…