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Found 18 papers, 8 papers with code
A Learning-based Framework for Hybrid Depth-from-Defocus and Stereo Matching
Depth from defocus (DfD) and stereo matching are two most studied passive depth sensing schemes.
Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image.
Deep Eyes: Binocular Depth-from-Focus on Focal Stack Pairs
We then construct three individual networks: a Focus-Net to extract depth from a single focal stack, a EDoF-Net to obtain the extended depth of field (EDoF) image from the focal stack, and a Stereo-Net to conduct stereo matching.
Photo-Realistic Facial Details Synthesis from Single Image
Our technique employs expression analysis for proxy face geometry generation and combines supervised and unsupervised learning for facial detail synthesis.
3D Face Reconstruction Using Color Photometric Stereo with Uncalibrated Near Point Lights
We avoid the need for spatial constancy of albedo; instead, we use a new measure for albedo similarity that is based on the albedo norm profile.
A Neural Rendering Framework for Free-Viewpoint Relighting
We present a novel Relightable Neural Renderer (RNR) for simultaneous view synthesis and relighting using multi-view image inputs.
SofGAN: A Portrait Image Generator with Dynamic Styling
To address this issue, we propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space.
Light Field-Based Underwater 3D Reconstruction Via Angular Resampling
Recovering 3D geometry of underwater scenes is challenging because of non-linear refraction of light at the water-air interface caused by the camera housing.
Multiresolution Deep Implicit Functions for 3D Shape Representation
To the best of our knowledge, MDIF is the first deep implicit function model that can at the same time (1) represent different levels of detail and allow progressive decoding; (2) support both encoder-decoder inference and decoder-only latent optimization, and fulfill multiple applications; (3) perform detailed decoder-only shape completion.
C-CAM: Causal CAM for Weakly Supervised Semantic Segmentation on Medical Image
Our method is motivated by two cause-effect chains including category-causality chain and anatomy-causality chain.
NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields
Visually exploring in a real-world 4D spatiotemporal space freely in VR has been a long-term quest.
Harnessing Low-Frequency Neural Fields for Few-Shot View Synthesis
We further propose a simple-yet-effective strategy for tuning the frequency to avoid overfitting few-shot inputs: enforcing consistency among the frequency domain of rendered 2D images.
High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition
To capture high-frequency personalized details, we transform the 3D mesh into the frequency domain, and propose a novel frequency decomposition loss to supervise each frequency component.
Multi-discontinuous Functional based Sliding Mode Cascade Observer for Estimation and Closed-loop Compensation Controller
The proposed observer consists of two layers: the first layer is a traditional sliding mode observer, and the second layer is a cascade observer.
NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions
The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals.
Relit-NeuLF: Efficient Relighting and Novel View Synthesis via Neural 4D Light Field
A DecomposeNet learns to map each ray to its SVBRDF components: albedo, normal, and roughness.
Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis
Novel view synthesis of dynamic scenes has been an intriguing yet challenging problem.
PERL: Parameter Efficient Reinforcement Learning from Human Feedback
We investigate the setup of "Parameter Efficient Reinforcement Learning" (PERL), in which we perform reward model training and reinforcement learning using LoRA.
