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Lina Liu

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3 papers

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AAAI Conference 2025 Conference Paper

Dual-branch Graph Feature Learning for NLOS Imaging

Xiongfei Su
Tianyi Zhu
Lina Liu
Zheng Chen
Yulun Zhang
Siyuan Li
Juntian Ye
Feihu Xu

The domain of non-line-of-sight (NLOS) imaging is advancing rapidly, offering the capability to reveal occluded scenes that are not directly visible. However, contemporary NLOS systems face several significant challenges: (1) The computational and storage requirements are profound due to the inherent three-dimensional grid data structure, which restricts practical application. (2) The simultaneous reconstruction of albedo and depth information requires a delicate balance using hyperparameters in the loss function, rendering the concurrent reconstruction of texture and depth information difficult. This paper introduces the innovative methodology, DG-NLOS, which integrates an albedo-focused reconstruction branch dedicated to albedo information recovery and a depth-focused reconstruction branch that extracts geometrical structure, to overcome these obstacles. The dual-branch framework segregates content delivery to the respective reconstructions, thereby enhancing the quality of the retrieved data. To our knowledge, we are the first to employ the GNN as a fundamental component to transform dense NLOS grid data into sparse structural features for efficient reconstruction. Comprehensive experiments demonstrate that our method attains the highest level of performance among existing methods across synthetic and real data.

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AAAI Conference 2021 Conference Paper

FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion

Lina Liu
Xibin Song
Xiaoyang Lyu
Junwei Diao
Mengmeng Wang
Yong Liu
Liangjun Zhang

Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate depth completion as a one-stage end-to-end learning task, which outputs dense depth maps directly. However, the feature extraction and supervision in one-stage frameworks are insufficient, limiting the performance of these approaches. To address this problem, we propose a novel end-to-end residual learning framework, which formulates the depth completion as a two-stage learning task, i. e. , a sparse-to-coarse stage and a coarse-tofine stage. First, a coarse dense depth map is obtained by a simple CNN framework. Then, a refined depth map is further obtained using a residual learning strategy in the coarse-tofine stage with a coarse depth map and color image as input. Specially, in the coarse-to-fine stage, a channel shuffle extraction operation is utilized to extract more representative features from the color image and coarse depth map, and an energy based fusion operation is exploited to effectively fuse these features obtained by channel shuffle operation, thus leading to more accurate and refined depth maps. We achieve SoTA performance in RMSE on KITTI benchmark. Extensive experiments on other datasets future demonstrate the superiority of our approach over current state-of-the-art depth completion approaches.

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AAAI Conference 2021 Conference Paper

HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation

Xiaoyang Lyu
Liang Liu
Mengmeng Wang
Xin Kong
Lina Liu
Yong Liu
Xinxin Chen
Yi Yuan

Self-supervised learning shows great potential in monocular depth estimation, using image sequences as the only source of supervision. Although people try to use high-resolution image for depth estimation, the accuracy of prediction has not been significantly improved. In this work, we find the core reason comes from the inaccurate depth estimation in large gradient regions, making the bilinear interpolation error gradually disappear as the resolution increases. To obtain more accurate depth estimation in large gradient regions, it is necessary to obtain high-resolution features with spatial and semantic information. Therefore, we present an improved DepthNet, HR-Depth, with two effective strategies: (1) redesign the skip-connection in DepthNet to get better highresolution features and (2) propose feature fusion Squeezeand-Excitation(fSE) module to fuse feature more efficiently. Using Resnet-18 as the encoder, HR-Depth surpasses all previous state-of-the-art(SoTA) methods with the least parameters at both high and low resolution. Moreover, previous SoTA methods are based on fairly complex and deep networks with many parameters which limits their real applications. Thus we also construct a lightweight network which uses MobileNetV3 as encoder. Experiments show that the lightweight network can perform on par with many large models like Monodepth2 at high-resolution with only 20% parameters. All codes and models will be available at https: //github. com/shawLyu/HR-Depth.

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