Implicit Neural Representations (INRs) model images as continuous functions that map spatial coordinates to pixel values, providing a compact and resolution-independent framework for image representation. Existing INR methods have achieved strong per...
The storage of medical images is one of the challenges in the medical imaging field. There are variable works that use implicit neural representation (INR) to compress volumetric medical images. However, there is room to improve the compression rate...
This paper presents an Implicit Neural Representation method for Event-Based Imaging Velocimetry (INR-VG) to reconstruct dense velocity fields from sparse event streams. The core idea is to learn a mapping (multilayer perceptron) from spatial coordin...
Deep learning methods based on implicit neural representations offer an efficient and automated solution for video inpainting by leveraging the inherent characteristics of video data. However, the limited size of the video embedding (e.g.,
Recently, end-to-end deep models for video compression have made steady advancements. However, this resulted in a lengthy and complex pipeline containing numerous redundant parameters. The video compression approaches based on implicit neural represe...
Deafferentation induced by local anesthesia causes a larger perceived area than the real area of the mouth, which, in the perspective of body representation, belongs to implicit body representation. In this study, we applied repetitive peripheral mag...
As a crucial component of multimodal sensing in modern AI agents, remote sensing images have attracted significant attention, for which neural representation is a promising direction. Implicit Neural Representations (INRs) using Multi-Layer Perceptro...
Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks. In recent years, denoising methods based on low-rank subspaces...
Implicit neural representations (INRs) are a new way to represent all kinds of signals ranging from 1D audio to 3D shape signals, among which 2D images are the most widely explored due to their ubiquitous presence. Image INRs utilize a neural network...
Image Implicit Neural Representations (INRs) adopt a neural network to learn a continuous function for mapping the pixel coordinates to their corresponding values. This task has gained significant attention for representing images in a continuous man...
In this paper, we propose a 3 dimensional (3D) model identification method based on weighted implicit shape representation (WISR) and panoramic view. The WISR is used for 3D shape normalization. The 3D shape normalization method normalizes a 3D model...
Remote sensing images often have limited resolution, which can hinder their effectiveness in various applications. Super-resolution techniques can enhance the resolution of remote sensing images, and arbitrary resolution super-resolution techniques p...
The raw data from ground-based telescopic optical observations serve as a key foundation for the analysis and identification of optical scattering properties of space objects, providing an essential guarantee for object identification and state predi...
This study addresses the insufficient representation of key regions, such as shock waves and boundary layers, in classical implicit neural representations (INRs) for super-resolution reconstruction of complex aerodynamic flow fields. A physics-aware...
Hyperspectral–multispectral (HSI–MSI) image fusion aims to reconstruct high-spatial-resolution hyperspectral images (HR-HSIs) by combining the spectral fidelity of low-resolution HSIs (LR-HSIs) with the spatial details of high-resolution...
Magnetic resonance imaging (MRI), a widely adopted modality in clinical practice, enables the acquisition of multi-contrast images from the same anatomical structure, commonly referred to as multimodal images. Integrating these diverse modalities is...
Recently, generating dense maps in real-time has become a hot research topic in the mobile robotics community, since dense maps can provide more informative and continuous features compared with sparse maps. Implicit depth representation (e.g., the d...
Biases in high-spatiotemporal-resolution radar quantitative precipitation estimation (QPE) can substantially compromise the reliability of meteorological and hydrological disaster warning. Existing QPE correction methods commonly assume predefined sp...
Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often...
Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associ...
The growing diversification of family structures has increased the prevalence of stepfamilies, yet implicit representations of stepfather and stepmother figures remain understudied, particularly among adolescents and emerging adults. This study exami...
Continuous implicit representations can flexibly describe complex 3D geometry and offer excellent potential for 3D point cloud analysis. However, it remains challenging for existing point-based deep learning architectures to leverage the implicit rep...
Implicit Neural Representations (INRs) provide a flexible and resolution-independent formulation for continuous signal representation. Despite their strong representation ability, standard INRs usually predict each queried coordinate independently, m...
Reconstructing neuronal morphology from microscopy image stacks is essential for understanding brain function and behavior. While existing methods are capable of tracking neuronal tree structures and creating membrane surface meshes, they often lack...
Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous...
Constructing a surface representation from the sparse point cloud of a satellite is an important task for satellite on-orbit services such as satellite docking and maintenance. In related studies on surface reconstruction from point clouds, implicit...
Non-rigid image registration is a fundamental problem in medical imaging and a representative example of continuous transformation modeling in image processing. Diffeomorphic registration methods, such as Large Deformation Diffeomorphic Metric Mappin...
We propose and validate a method to find an implicit representation of a surface placed at a distance h from another implicit surface. With two such surfaces on either side of the original surface, a volumetric shell of predefined thickness can be ob...
Deformable image registration (DIR) is central to medical image analysis, supporting spatial alignment for longitudinal studies and multi-modal fusion. Learning-based methods such as CNNs and transformers provide rapid inference but often require lar...
Discovering tactical knowledge aims to extract tactical data derived from battlefield signal data, which is vital in information warfare. The learning and reasoning from battlefield signal information can help commanders make effective decisions. How...
Grammatical language development in individuals with autism (without intellectual/language impairment) is mostly qualitatively comparable to language development in typically developing children of the same age. The majority of tasks used to study gr...
It is well-known that activation functions are crucial in determining spectral expressiveness, training dynamics, and reconstruction accuracy in implicit neural representations (INRs), which employ coordinate-based multilayer perceptrons to represent...
While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before en...
Traditional social recommendation methods often focus on static representations of users and items, neglecting dynamic changes in user interests and item attractiveness over time, which makes it challenging to adapt to temporal variations in user int...
Robust blind locomotion over complex unstructured terrains relies on accurate estimation of robot states and surrounding terrain geometry. However, under real-world deployment conditions without exteroceptive perception, it remains challenging to acc...
Representation learning-based collaborative filtering (CF) methods address the linear relationship of user-items with dot products and cannot study the latent nonlinear relationship applied to implicit feedback. Matching function learning-based CF me...
Developing a high-quality, real-time, dense visual SLAM system poses a significant challenge in the field of computer vision. NeRF introduces neural implicit representation, marking a notable advancement in visual SLAM research. However, existing neu...
To accurately estimate the 6D pose of objects, most methods employ a two-stage algorithm. While such two-stage algorithms achieve high accuracy, they are often slow. Additionally, many approaches utilize encoding–decoding to obtain the 6D pose,...
Pansharpening seeks to reconstruct a high-resolution multi-spectral image (HR-MSI) by integrating the fine spatial details from the panchromatic (PAN) image with the spectral richness of the low-resolution multi-spectral image (LR-MSI). In recent yea...
Hyperspectral image (HSI) classification is important for land-cover recognition and remote-sensing scene analysis. However, class imbalance severely limits performance when minority classes contain only a few labeled samples, causing models to show...
The insertion of proteins into membranes is crucial for understanding their function in many biological processes. In this work, we present UNILIPID, a universal implicit lipid-protein description as a methodology for dealing with implicit membranes....
Existing approaches for image deraining often rely on synthetic or unpaired real-world rainy datasets, leading to sub-optimal generalization ability when processing the complex and diverse real-world rain degradation. To address these challenges, we...
Optical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem u...
Recent advancements in implicit neural representations have shown substantial promise in various domains, particularly in video compression and reconstruction, due to their rapid decoding speed and high adaptability. Building upon the state-of-the-ar...
With the advent of generative AI, medical imaging has been revolutionized, allowing for unprecedented capabilities in data generation, volumetric reconstruction, and clinical decision support. Although significant advances have been achieved in two-d...
Considering the Weierstrass data as
This paper proposes a new CAD model for the design of lattice material components. The CAD model better captures the user’s design intent and provides a dual-scale framework to represent the geometry and material distribution. Conventional CAD...
This work considers the Minkowski sum of an N-dimensional ellipsoid and hyperrectangle, a combination that is extremely relevant due to the usage of ellipsoid-adjacent primitives in computer graphics for work such as 3D Gaussian splatting. While para...
Generative steganography (GS) generates stego-media via secret messages, but existing GS only targets single-type multimedia data with poor universality. The generator and extractor sizes are highly coupled with resolution. Message mapping converts s...
The study of moral conceptual metaphors has been an important topic in recent years. In Chinese culture, the concepts of curvature and straightness are given certain semantic contents, in which curvature refers to being sly while straightness refers...
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