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Claim this profileWorks175 from public data
- DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image437
A deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth, which improves upon the state-of-the-art performance on KITTI depth completion benchmark.
- Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation429
A hierarchical network with recurrent refinement to update disparities in a coarse-to-fine manner, as well as a stacked cascaded architecture for inference and a new synthetic dataset with special attention to difficult cases for better generalizing to real-world scenes are introduced.
- Bundled camera paths for video stabilization410
A novel video stabilization method which models camera motion with a bundle of (multiple) camera paths based on a mesh-based, spatially-variant motion representation and an adaptive, space-time path optimization and introduces the 'as-similar-as-possible' idea to make motion estimation more robust.
- Low-Light Image Enhancement with Wavelet-Based Diffusion Models385
This work presents a wavelet-based conditional diffusion model (WCDM) that leverages the generative power of diffusion models to produce results with satisfactory perceptual fidelity and takes advantage of the strengths of wavelet transformation to greatly accelerate inference and reduce computational resource usage without sacrificing information.
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- NBNet: Noise Basis Learning for Image Denoising with Subspace Projection238
NBNet is introduced, a novel framework for image denoising designed to train a network that can separate signal and noise by learning a set of reconstruction basis in the feature space, and SSA, a non-local attention module designed to explicitly learn the basis generation as well as subspace projection.
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- OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration218
OM-Net is presented, a global feature based iterative network for partial- to-partial point cloud registration that learns overlapping masks to reject non-overlapping regions, which converts the partial-to-partial registration to the registration of the same shape.
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- Content-Aware Unsupervised Deep Homography Estimation204
This work proposes an unsupervised deep homography method with a new architecture design that outperforms the state-of-the-art including deep solutions and feature-based solutions.
- DaST: Data-Free Substitute Training for Adversarial Attacks179
This paper is the first to train a substitute model for adversarial attacks without any real data and can achieve competitive performance compared with the baseline models which are trained by the same train set with attacked models.
- MeshFlow: Minimum Latency Online Video Stabilization160
The quantitative and qualitative evaluations show that the proposed technique for online video stabilization with only one frame latency using a novel MeshFlow motion model can produce comparable results with the state-of-the-art off-line methods.
- Deep Constrained Least Squares for Blind Image Super-Resolution144
This paper reformulate the degradation model such that the deblurred kernel estimation can be transferred into the low resolution space and introduces a dynamic deep linear filter module that can adaptively generate deblurring kernel weights conditional on the input and yields more robust kernel estimation.
- Holistic 3D Scene Understanding from a Single Image with Implicit Representation144
This work proposes an image-based local structured implicit network to improve the object shape estimation, but also refine the 3D object pose and scene layout via a novel implicit scene graph neural network that exploits the implicit local object features.
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- Parallax-Tolerant Unsupervised Deep Image Stitching134
This work proposes a parallax-tolerant unsupervised deep image stitching technique that is parallax-tolerant and free from laborious designs of complicated geometric features for specific scenes, both quantitatively and qualitatively.
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- ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging116
This paper presents an attention-guided deformable convolutional network for hand-held multi-frame high dynamic range (HDR) imaging, namely ADNet, and proposes to align the gamma-corrected images in the feature-level with a Pyramid, Cascading and Deformable alignment module.
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- Depth-Aware Multi-Grid Deep Homography Estimation With Contextual Correlation108
A contextual correlation layer (CCL) is designed that can efficiently capture the long-range correlation within feature maps and can be flexibly used in a learning framework to predict multi-grid homography from global to local.
- OAENet: Oriented attention ensemble for accurate facial expression recognition101
This work proposes an oriented attention pseudo-siamese network that takes advantage of global and local facial information for high accurate FER and has achieved a very appealing performance compared to other state-of-the-art methods.
- Neural Point Cloud Rendering via Multi-Plane Projection100
A new deep point cloud rendering pipeline through multi-plane projections to project features from 3D points onto 2D image domain into a layered volume of camera frustum, such that ghosting effects due to false visibility check as well as occlusions caused by noise interferences are both avoided successfully.
- UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning96
This work designs a self-guided upsample module to tackle the interpolation blur problem caused by bilinear upsampling between pyramid levels, and proposes a pyramid distillation loss to add supervision for intermediate levels via distilling the finest flow as pseudo labels.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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