no code implementations • 13 Mar 2024 • Jing Wu, Jia-Wang Bian, Xinghui Li, Guangrun Wang, Ian Reid, Philip Torr, Victor Adrian Prisacariu
We propose GaussCtrl, a text-driven method to edit a 3D scene reconstructed by the 3D Gaussian Splatting (3DGS).
no code implementations • 23 Dec 2023 • Kaichen Zhou, Jia-Xing Zhong, Jia-Wang Bian, Qian Xie, Jian-Qing Zheng, Niki Trigoni, Andrew Markham
Despite advancements in self-supervised monocular depth estimation, challenges persist in dynamic scenarios due to the dependence on assumptions about a static world.
1 code implementation • 11 Oct 2023 • Jia-Wang Bian, Wenjing Bian, Victor Adrian Prisacariu, Philip Torr
On the MobileBrick dataset that contains casually captured unbounded 360-degree videos, our method refines ARKit poses and improves the reconstruction F1 score from 69. 18 to 75. 67, outperforming that with the dataset provided ground-truth pose (75. 14).
1 code implementation • CVPR 2023 • Kejie Li, Jia-Wang Bian, Robert Castle, Philip H. S. Torr, Victor Adrian Prisacariu
The distinct data modality offered by high-resolution RGB images and low-resolution depth maps captured on a mobile device, when combined with precise 3D geometry annotations, presents a unique opportunity for future research on high-fidelity 3D reconstruction.
no code implementations • 14 Dec 2022 • Le Zhang, Qibin Hou, Yun Liu, Jia-Wang Bian, Xun Xu, Joey Tianyi Zhou, Ce Zhu
Ensemble learning serves as a straightforward way to improve the performance of almost any machine learning algorithm.
1 code implementation • CVPR 2023 • Wenjing Bian, ZiRui Wang, Kejie Li, Jia-Wang Bian, Victor Adrian Prisacariu
Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes.
2 code implementations • 7 Nov 2022 • Libo Sun, Jia-Wang Bian, Huangying Zhan, Wei Yin, Ian Reid, Chunhua Shen
Self-supervised monocular depth estimation has shown impressive results in static scenes.
Indoor Monocular Depth Estimation Monocular Depth Estimation +1
1 code implementation • 29 Jul 2022 • Guangkai Xu, Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Jia-Wang Bian
Our method leverages a data-driven prior in the form of a single image depth prediction network trained on large-scale datasets, the output of which is used as an input to our model.
2 code implementations • 25 May 2021 • Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, Ian Reid
We propose a monocular depth estimator SC-Depth, which requires only unlabelled videos for training and enables the scale-consistent prediction at inference time.
2 code implementations • 1 Mar 2021 • Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian, Ravi Garg, Ian Reid
More surprisingly, they show that the well-trained networks enable scale-consistent predictions over long videos, while the accuracy is still inferior to traditional methods because of ignoring geometric information.
1 code implementation • 24 Dec 2020 • Yu-Huan Wu, Yun Liu, Jun Xu, Jia-Wang Bian, Yu-Chao Gu, Ming-Ming Cheng
Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD.
no code implementations • 21 Dec 2020 • Xinyu Zhang, Xinlong Wang, Jia-Wang Bian, Chunhua Shen, Mingyu You
Person search aims to localize and identify a specific person from a gallery of images.
1 code implementation • 4 Jun 2020 • Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Tat-Jun Chin, Chunhua Shen, Ian Reid
However, excellent results have mostly been obtained in street-scene driving scenarios, and such methods often fail in other settings, particularly indoor videos taken by handheld devices.
Ranked #62 on Monocular Depth Estimation on NYU-Depth V2
no code implementations • 24 Dec 2019 • Le Zhang, Zenglin Shi, Joey Tianyi Zhou, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Zeng Zeng, Chunhua Shen
Specifically, with a diagnostic analysis, we show that the recurrent structure may not be effective to learn temporal dependencies than what we expected and implicitly yields an orderless representation.
no code implementations • 27 Nov 2019 • Xin-Yu Zhang, Le Zhang, Zao-Yi Zheng, Yun Liu, Jia-Wang Bian, Ming-Ming Cheng
The effectiveness of the triplet loss heavily relies on the triplet selection, in which a common practice is to first sample intra-class patches (positives) from the dataset for batch construction and then mine in-batch negatives to form triplets.
2 code implementations • 21 Sep 2019 • Huangying Zhan, Chamara Saroj Weerasekera, Jia-Wang Bian, Ian Reid
In this work we present a monocular visual odometry (VO) algorithm which leverages geometry-based methods and deep learning.
2 code implementations • NeurIPS 2019 • Jia-Wang Bian, Zhichao Li, Naiyan Wang, Huangying Zhan, Chunhua Shen, Ming-Ming Cheng, Ian Reid
To the best of our knowledge, this is the first work to show that deep networks trained using unlabelled monocular videos can predict globally scale-consistent camera trajectories over a long video sequence.
Ranked #61 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 26 Aug 2019 • Jia-Wang Bian, Yu-Huan Wu, Ji Zhao, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid
According to this, we propose three high-quality matching systems and a Coarse-to-Fine RANSAC estimator.
no code implementations • 24 Aug 2019 • Le Zhang, Zenglin Shi, Ming-Ming Cheng, Yun Liu, Jia-Wang Bian, Joey Tianyi Zhou, Guoyan Zheng, Zeng Zeng
Nonlinear regression has been extensively employed in many computer vision problems (e. g., crowd counting, age estimation, affective computing).
no code implementations • 28 Dec 2018 • Yun Liu, Yu Qiu, Le Zhang, Jia-Wang Bian, Guang-Yu Nie, Ming-Ming Cheng
In this paper, we observe that the contexts of a natural image can be well expressed by a high-to-low self-learning of side-output convolutional features.
no code implementations • 7 Aug 2018 • Jia-Wang Bian, Ruihan Yang, Yun Liu, Le Zhang, Ming-Ming Cheng, Ian Reid, WenHai Wu
This leads to a critical absence in this field that there is no standard datasets and evaluation metrics to evaluate different feature matchers fairly.
no code implementations • 19 May 2018 • Yun Liu, Yujun Shi, Jia-Wang Bian, Le Zhang, Ming-Ming Cheng, Jiashi Feng
Collecting sufficient annotated data is very expensive in many applications, especially for pixel-level prediction tasks such as semantic segmentation.
no code implementations • 12 Sep 2017 • Jia-Wang Bian, Le Zhang, Yun Liu, Wen-Yan Lin, Ming-Ming Cheng, Ian D. Reid
To this end, we present a uniform benchmark with novel evaluation metrics and a large-scale dataset for evaluating the overall performance of image matching methods.
1 code implementation • CVPR 2017 • Jia-Wang Bian, Wen-Yan Lin, Yasuyuki Matsushita, Sai-Kit Yeung, Tan-Dat Nguyen, Ming-Ming Cheng
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching.