Author name cluster

Junda Cheng

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4 papers

2 author rows

IROS Conference 2025 Conference Paper

ACP-MVS: Efficient Multi-View Stereo with Attention-based Context Perception

Hao Jia
Gangwei Xu
Miaojie Feng
Xianqi Wang 0001
Junda Cheng
Min Lin 0006
Xin Yang 0008

The core of Multi-View Stereo (MVS) is to find corresponding pixels in neighboring images. However, due to challenging regions in input images such as untextured areas, repetitive patterns, or reflective surfaces, existing methods struggle to find precise pixel correspondence therein, resulting in inferior reconstruction quality. In this paper, we present an efficient context-perception MVS network, termed ACP-MVS. The ACP-MVS constructs a context-aware cost volume that can enhance pixels containing essential context information while suppressing irrelevant or noisy information via our proposed Context-stimulated Weighting Fusion module. Furthermore, we introduce a new Context-Guided Global Aggregation module, based on the insight that similar-looking pixels tend to have similar depths, which exploits global contextual cues to implicitly guide depth detail propagation from high-confidence regions to low-confidence ones. These two modules work in synergy to substantially improve reconstruction quality of ACP-MVS without incurring significant additional computational and time cost. Extensive experiments demonstrate that our approach not only achieves state-of-the-art performance but also offers the fastest inference speed and minimal GPU memory usage, providing practical value for practitioners working with high-resolution MVS image sets. Notably, our method ranks 2nd on the challenging Tanks and Temples advanced benchmark among all published methods. Code is available at https://github.com/HaoJia-mongh/ACP-MVS.

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AAAI Conference 2025 Conference Paper

Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual Odometry

Zhaoxing Zhang
Junda Cheng
Gangwei Xu
Xiaoxiang Wang
Can Zhang
Xin Yang

Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noisy and inconsistent flow matching, making it difficult to handle challenging scenarios and long-sequence estimation.To overcome these challenges, we introduce Spatio-Temporal Visual Odometry (STVO), a novel deep network architecture that effectively leverages inherent spatio-temporal cues to enhance the accuracy and consistency of multi-frame flow matching. With more accurate and consistent flow matching, STVO can achieve better pose estimation through the bundle adjustment (BA).Specifically, STVO introduces two innovative components: 1) the Temporal Propagation Module that utilizes multi-frame information to extract and propagate temporal cues across adjacent frames, maintaining temporal consistency; 2) the Spatial Activation Module that utilizes geometric priors from the depth maps to enhance spatial consistency while filtering out excessive noise and incorrect matches.Our STVO achieves state-of-the-art performance on TUM-RGBD, EuRoc MAV, ETH3D and KITTI Odometry benchmarks. Notably, it improves accuracy by 77.8% on ETH3D benchmark and 38.9% on KITTI Odometry benchmark over the previous best methods.

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IROS Conference 2025 Conference Paper

LiDAR-Inertial Odometry in Dynamic Driving Scenarios using Label Consistency Detection

Zikang Yuan
Xiaoxiang Wang
Jingying Wu
Junda Cheng
Xin Yang

In this paper, a LiDAR-inertial odometry (LIO) method that eliminates the influence of moving objects in dynamic driving scenarios is proposed. This method constructs binarized labels for 3D points of current sweep, and utilizes the label difference between each point and its surrounding points in global map to identify moving objects. The surrounding points in global map are localized by voxel-location-based nearest neighbor search, without involving any massive computations. In addition, the proposed method is embeded into a LIO system (i. e. , Dynamic-LIO), and achieves state-of-the-art performance on public datasets with extremlely low computational overhead (i. e. , 1~9ms/sweep). We have released the source code of this work for the development of the community.

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IROS Conference 2020 Conference Paper

D 2 VO: Monocular Deep Direct Visual Odometry

Qizeng Jia
Yuechuan Pu
Jingyu Chen
Junda Cheng
Chunyuan Liao
Xin Yang 0008

In this paper, we present a novel deep learning and direct method based monocular visual odometry system named D 2 VO. Our system reconstructs the dense depth map of each keyframe and tracks camera poses based on these keyframes. Combining direct method and deep learning, both tracking and mapping of the system could benefit from the geometric measurement and semantic information. For each input frame, a feature pyramid is built and shared by both tracking and mapping process. The depth map of keyframe is efficiently estimated from coarse to fine with the followed multi-view hierarchical depth estimation network. We optimize the camera pose by minimizing photometric error between re-projected features of each frame and its reference keyframe with bundle adjustment. Experimental results on TUM dataset demonstrate that our approach outperforms the state-of-the-art methods on both tracking and mapping.

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