Search Results for author: Jia-Wang Bian

Found 25 papers, 14 papers with code

GaussCtrl: Multi-View Consistent Text-Driven 3D Gaussian Splatting Editing

1 code implementation13 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).

MGDepth: Motion-Guided Cost Volume For Self-Supervised Monocular Depth In Dynamic Scenarios

no code implementations23 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.

Computational Efficiency Monocular Depth Estimation +1

PoRF: Pose Residual Field for Accurate Neural Surface Reconstruction

1 code implementation11 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).

Multi-View 3D Reconstruction Neural Rendering +2

MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices

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.

3D Object Reconstruction 3D Reconstruction +1

Deep Negative Correlation Classification

no code implementations14 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.

Classification Diversity +1

NoPe-NeRF: Optimising Neural Radiance Field with No Pose Prior

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.

Pose Estimation

Towards Domain-agnostic Depth Completion

1 code implementation29 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.

Depth Completion Depth Estimation +2

Unsupervised Scale-consistent Depth Learning from Video

2 code implementations25 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.

Monocular Depth Estimation Monocular Visual Odometry +1

DF-VO: What Should Be Learnt for Visual Odometry?

2 code implementations1 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.

Monocular Visual Odometry Optical Flow Estimation

MobileSal: Extremely Efficient RGB-D Salient Object Detection

1 code implementation24 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.

Object object-detection +2

Auto-Rectify Network for Unsupervised Indoor Depth Estimation

1 code implementation4 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.

Monocular Depth Estimation Self-Supervised Learning +1

Ordered or Orderless: A Revisit for Video based Person Re-Identification

no code implementations24 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.

Video-Based Person Re-Identification

AdaSample: Adaptive Sampling of Hard Positives for Descriptor Learning

no code implementations27 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.


Visual Odometry Revisited: What Should Be Learnt?

2 code implementations21 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.

Monocular Visual Odometry

Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video

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.

Camera Pose Estimation Depth And Camera Motion +2

An Evaluation of Feature Matchers for Fundamental Matrix Estimation

no code implementations26 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.

Robust Regression via Deep Negative Correlation Learning

no code implementations24 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).

Age Estimation Crowd Counting +2

Salient Object Detection via High-to-Low Hierarchical Context Aggregation

no code implementations28 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.

object-detection RGB Salient Object Detection +4

MatchBench: An Evaluation of Feature Matchers

no code implementations7 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.

Learning Pixel-wise Labeling from the Internet without Human Interaction

no code implementations19 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.

Segmentation Semantic Segmentation

Image Matching: An Application-oriented Benchmark

no code implementations12 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.

Attribute Benchmarking +1

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