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Claim this profileWorks121 from public data
- Low-Light Image and Video Enhancement Using Deep Learning: A Survey651
This paper proposes a low-light image and video dataset, in which the images and videos are taken by different mobile phones’ cameras under diverse illumination conditions, and provides a unified online platform that covers many popular LLIE methods.
- Geometry-Aware Learning of Maps for Camera Localization456
This work proposes to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation and proposes a novel parameterization for camera rotation which is better suited for deep-learning based camera pose regression.
- MapNet: Geometry-Aware Learning of Maps for Camera Localization456
This work proposes to represent maps as a deep neural net called MapNet, which enables learning a data-driven map representation and proposes a novel parameterization for camera rotation which is better suited for deep-learning based camera pose regression.
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- GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution305
This work shows that pre-trained Generative Adversarial Networks (GANs), e.g., StyleGAN, can be used as a latent bank to improve the restoration quality of large-factor image super-resolution (SR) and shows clear improvements in terms of fidelity and texture faithfulness in comparison to existing methods.
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- PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image263
A deep neural architecture that detects and reconstructs piecewise planar regions from a single RGB image using a variant of Mask R-CNN and refines an arbitrary number of segmentation masks with a novel loss enforcing the consistency with a nearby view during training.
- World Simulation with Video Foundation Models for Physical AI211
This work introduces [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI, which unifies Text2World, Image2World, and Video2World generation in a single model and leverages [Cosmos-Reason1], a Physical AI vision-language model, to provide richer text grounding and finer control of world simulation.
- Neural Inverse Rendering of an Indoor Scene From a Single Image183
This work proposes the first learning based approach that jointly estimates albedo, normals, and lighting of an indoor scene from a single image, and uses physically-based rendering to create a large-scale synthetic dataset, named SUNCG-PBR, which is a significant improvement over prior datasets.
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- Neural RGB®D Sensing: Depth and Uncertainty From a Video Camera156
This paper proposes a deep learning method to estimate per-pixel depth and its uncertainty continuously from a monocular video stream, with the goal of effectively turning an RGB camera into an RGB-D camera.
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- Time-varying surface appearance122
This paper describes the acquisition of the first time-varying database of 26 samples, encompassing a variety of natural processes including burning, drying, decay, and corrosion and derives an overall temporal appearance variation characteristic curve of the specific process, as well as space-dependent textures, rates, and offsets.
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- Printing spatially-varying reflectance104
This work proposes a complete system that uses appropriate inks and foils to print documents with a variety of material properties, and automatically finds the optimal linear combinations to approximate the BRDFs of the target documents.
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- A combination model for orientation field of fingerprints100
A polynomial model is used to approximate the orientation field globally and a point-charge model at each singular point to improve the approximation locally and this combination model is more accurate and robust with respect to noise compared with the previous works.
- Neighbourhood Representative Sampling for Efficient End-to-End Video Quality Assessment99
This work proposes a unified scheme, spatial-temporal grid mini-cube sampling (St-GMS), and the resultant samples are named fragments, a network architecture tailored specifically for fragments that achieves up to 1612× efficiency than the existing state-of-the-art VQA benchmarks.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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