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Found 54 papers, 17 papers with code
Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation
The integration of deep learning in medical imaging has shown great promise for enhancing diagnostic, therapeutic, and research outcomes.
NeRF-Texture: Synthesizing Neural Radiance Field Textures
This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance.
SuperGaussians: Enhancing Gaussian Splatting Using Primitives with Spatially Varying Colors
However, the current Gaussian primitives only have a single view-dependent color and an opacity to represent the appearance and geometry of the scene, resulting in a non-compact representation.
Real-time 3D-aware Portrait Video Relighting
We demonstrate the effectiveness and interactivity of our method on various portrait videos with diverse lighting and viewing conditions.
4Dynamic: Text-to-4D Generation with Hybrid Priors
The static 3D generation is achieved under the guidance of the input text and the first frame of the reference video, while in the dynamic generation stage, we introduce a customized SDS loss to ensure multi-view consistency, a video-based SDS loss to improve temporal consistency, and most importantly, direct priors from the reference video to ensure the quality of geometry and texture.
DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading
To address these issues, we introduce DeferredGS, a method for decoupling and editing the Gaussian splatting representation using deferred shading.
StylizedGS: Controllable Stylization for 3D Gaussian Splatting
As XR technology continues to advance rapidly, 3D generation and editing are increasingly crucial.
Recent Advances in 3D Gaussian Splatting
The emergence of 3D Gaussian Splatting (3DGS) has greatly accelerated the rendering speed of novel view synthesis.
Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes
However, common distance field based implicit representations, specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes.
Mesh-based Gaussian Splatting for Real-time Large-scale Deformation
Based on this representation, we further introduce a large-scale Gaussian deformation technique to enable deformable GS, which alters the parameters of 3D Gaussians according to the manipulation of the associated mesh.
Retargeting Visual Data with Deformation Fields
Seam carving is an image editing method that enable content-aware resizing, including operations like removing objects.
Tri-MipRF: Tri-Mip Representation for Efficient Anti-Aliasing Neural Radiance Fields
Despite the tremendous progress in neural radiance fields (NeRF), we still face a dilemma of the trade-off between quality and efficiency, e. g., MipNeRF presents fine-detailed and anti-aliased renderings but takes days for training, while Instant-ngp can accomplish the reconstruction in a few minutes but suffers from blurring or aliasing when rendering at various distances or resolutions due to ignoring the sampling area.
Sketch Beautification: Learning Part Beautification and Structure Refinement for Sketches of Man-made Objects
With this strategy, our method can go beyond the training samples and handle novel freehand sketches.
NeUDF: Leaning Neural Unsigned Distance Fields with Volume Rendering
Multi-view shape reconstruction has achieved impressive progresses thanks to the latest advances in neural implicit surface rendering.
LC-NeRF: Local Controllable Face Generation in Neural Randiance Field
Since changes to the latent code affect global generation results, these methods do not allow for fine-grained control of local facial regions.
SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry
We propose SCENEHGN, a hierarchical graph network for 3D indoor scenes that takes into account the full hierarchy from the room level to the object level, then finally to the object part level.
E3Sym: Leveraging E(3) Invariance for Unsupervised 3D Planar Reflective Symmetry Detection
E3Sym establishes robust point correspondences through the use of E(3) invariant features extracted from a lightweight neural network, from which the dense symmetry prediction is produced.
NeRFFaceEditing: Disentangled Face Editing in Neural Radiance Fields
The geometry decoder aligns the original facial volume with the semantic mask volume.
StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF.
NeRF-Editing: Geometry Editing of Neural Radiance Fields
In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network.
DrawingInStyles: Portrait Image Generation and Editing with Spatially Conditioned StyleGAN
The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques.
Socially-Optimal Mechanism Design for Incentivized Online Learning
Multi-arm bandit (MAB) is a classic online learning framework that studies the sequential decision-making in an uncertain environment.
High-Fidelity Point Cloud Completion with Low-Resolution Recovery and Noise-Aware Upsampling
Instead of decoding a whole shape, we propose to decode and refine a low-resolution (low-res) point cloud first, and then performs a patch-wise noise-aware upsampling rather than interpolating the whole sparse point cloud at once, which tends to lose details.
SAC-GAN: Structure-Aware Image Composition
Specifically, our network takes the semantic layout features from the input scene image, features encoded from the edges and silhouette in the input object patch, as well as a latent code as inputs, and generates a 2D spatial affine transform defining the translation and scaling of the object patch.
OctField: Hierarchical Implicit Functions for 3D Modeling
We achieve this goal by introducing a hierarchical octree structure to adaptively subdivide the 3D space according to the surface occupancy and the richness of part geometry.
Multi-sensor joint target detection, tracking and classification via Bernoulli filter
This paper focuses on \textit{joint detection, tracking and classification} (JDTC) of a target via multi-sensor fusion.
Autoregressive Stylized Motion Synthesis With Generative Flow
Motion style transfer is an important problem in many computer graphics and computer vision applications, including human animation, games, and robotics.
Deep Deformation Detail Synthesis for Thin Shell Models
Moreover, as such methods only add details, they require coarse meshes to be close to fine meshes, which can be either impossible, or require unrealistic constraints when generating fine meshes.
Single Image 3D Shape Retrieval via Cross-Modal Instance and Category Contrastive Learning
Inspired by the great success in recent contrastive learning works on self-supervised representation learning, we propose a novel IBSR pipeline leveraging contrastive learning.
Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders
The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales.
3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
Currently, 3D-FRONT contains 18, 968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets.
TM-NET: Deep Generative Networks for Textured Meshes
We introduce a conditional autoregressive model for texture generation, which can be conditioned on both part geometry and textures already generated for other parts to achieve texture compatibility.
Graphics
RISA-Net: Rotation-Invariant Structure-Aware Network for Fine-Grained 3D Shape Retrieval
Fine-grained 3D shape retrieval aims to retrieve 3D shapes similar to a query shape in a repository with models belonging to the same class, which requires shape descriptors to be capable of representing detailed geometric information to discriminate shapes with globally similar structures.
3D-FUTURE: 3D Furniture shape with TextURE
The 3D CAD shapes in current 3D benchmarks are mostly collected from online model repositories.
3D Object Reconstruction From A Single Image
3D Shape Retrieval
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DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structure, in a controllable manner.
Deep Generation of Face Images from Sketches
Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches.
Deep Line Art Video Colorization with a Few References
The color transform network takes the target line art images as well as the line art and color images of one or more reference images as input, and generates corresponding target color images.
STD-Net: Structure-preserving and Topology-adaptive Deformation Network for 3D Reconstruction from a Single Image
3D reconstruction from a single view image is a long-standing prob-lem in computer vision.
A Node Embedding Framework for Integration of Similarity-based Drug Combination Prediction
Results: In this paper, we proposed a Network Embedding framework in Multiplex Networks (NEMN) to predict synthetic drug combinations.
A Survey on Deep Geometry Learning: From a Representation Perspective
However, the performance for different applications largely depends on the representation used, and there is no unique representation that works well for all applications.
Graphics
Learning-based Real-time Detection of Intrinsic Reflectional Symmetry
In this paper, we propose \YL{a} learning-based approach to intrinsic reflectional symmetry detection.
LaplacianNet: Learning on 3D Meshes with Laplacian Encoding and Pooling
3D models are commonly used in computer vision and graphics.
PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models
In this paper, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape.
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding
We present a new algorithm to embed a high-dimensional configuration space of deformable objects in a low-dimensional feature space, where the configurations of objects and feature points have approximate one-to-one mapping.
5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion
5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station (BS) and vehicles are equipped with large antenna arrays.
SDM-NET: Deep Generative Network for Structured Deformable Mesh
At the structural level, we train a Structured Parts VAE (SP-VAE), which jointly learns the part structure of a shape collection and the part geometries, ensuring a coherence between global shape structure and surface details.
Mesh Variational Autoencoders with Edge Contraction Pooling
3D shape analysis is an important research topic in computer vision and graphics.
Joint Coverage and Power Control in Highly Dynamic and Massive UAV Networks: An Aggregative Game-theoretic Learning Approach
Unmanned aerial vehicles (UAV) ad-hoc network is a significant contingency plan for communication after a natural disaster, such as typhoon and earthquake.
VV-Net: Voxel VAE Net with Group Convolutions for Point Cloud Segmentation
Our approach results in a good volumetric representation that effectively tackles noisy point cloud datasets and is more robust for learning.
Graphics
Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences
In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs).
Graphics
Variational Autoencoders for Deforming 3D Mesh Models
3D geometric contents are becoming increasingly popular.
Graphics
Mesh-based Autoencoders for Localized Deformation Component Analysis
Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing.
Graphics
Sparse Data Driven Mesh Deformation
This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied.
Graphics
