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Found 34 papers, 12 papers with code
Controllable Dynamic Appearance for Neural 3D Portraits
The surface normals prediction is guided using 3DMM normals that act as a coarse prior for the normals of the human head, where direct prediction of normals is hard due to rigid and non-rigid deformations induced by head-pose and facial expression changes.
OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects
We introduce OpenIllumination, a real-world dataset containing over 108K images of 64 objects with diverse materials, captured under 72 camera views and a large number of different illuminations.
Strivec: Sparse Tri-Vector Radiance Fields
In contrast to TensoRF which uses a global tensor and focuses on their vector-matrix decomposition, we propose to utilize a cloud of local tensors and apply the classic CANDECOMP/PARAFAC (CP) decomposition to factorize each tensor into triple vectors that express local feature distributions along spatial axes and compactly encode a local neural field.
Neural Free-Viewpoint Relighting for Glossy Indirect Illumination
In this paper, we demonstrate a hybrid neural-wavelet PRT solution to high-frequency indirect illumination, including glossy reflection, for relighting with changing view.
One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization
Single image 3D reconstruction is an important but challenging task that requires extensive knowledge of our natural world.
NeuManifold: Neural Watertight Manifold Reconstruction with Efficient and High-Quality Rendering Support
We present a method for generating high-quality watertight manifold meshes from multi-view input images.
TensoIR: Tensorial Inverse Rendering
We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields.
MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field
Under the Lagrangian view, we parameterize the scene motion by tracking the trajectory of particles on objects.
Factor Fields: A Unified Framework for Neural Fields and Beyond
Our experiments show that DiF leads to improvements in approximation quality, compactness, and training time when compared to previous fast reconstruction methods.
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
In this paper, we present RenderDiffusion, the first diffusion model for 3D generation and inference, trained using only monocular 2D supervision.
PhotoScene: Photorealistic Material and Lighting Transfer for Indoor Scenes
Most indoor 3D scene reconstruction methods focus on recovering 3D geometry and scene layout.
RigNeRF: Fully Controllable Neural 3D Portraits
In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video.
ARF: Artistic Radiance Fields
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene.
Differentiable Rendering of Neural SDFs through Reparameterization
Our method leverages the distance to surface encoded in an SDF and uses quadrature on sphere tracer points to compute this warping function.
Physically-Based Editing of Indoor Scene Lighting from a Single Image
We tackle this problem using two novel components: 1) a holistic scene reconstruction method that estimates scene reflectance and parametric 3D lighting, and 2) a neural rendering framework that re-renders the scene from our predictions.
NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction
We demonstrate that NeRFusion achieves state-of-the-art quality on both large-scale indoor and small-scale object scenes, with substantially faster reconstruction than NeRF and other recent methods.
TensoRF: Tensorial Radiance Fields
We demonstrate that applying traditional CP decomposition -- that factorizes tensors into rank-one components with compact vectors -- in our framework leads to improvements over vanilla NeRF.
Point-NeRF: Point-based Neural Radiance Fields
Point-NeRF combines the advantages of these two approaches by using neural 3D point clouds, with associated neural features, to model a radiance field.
NeLF: Neural Light-transport Field for Portrait View Synthesis and Relighting
Our system is trained on a large number of synthetic models, and can generalize to different synthetic and real portraits under various lighting conditions.
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
NeuMIP: Multi-Resolution Neural Materials
We also introduce neural offsets, a novel method which allows rendering materials with intricate parallax effects without any tessellation.
MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo
We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis.
NeuTex: Neural Texture Mapping for Volumetric Neural Rendering
We achieve this by introducing a 3D-to-2D texture mapping (or surface parameterization) network into volumetric representations.
Light Stage Super-Resolution: Continuous High-Frequency Relighting
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces.
Photon-Driven Neural Path Guiding
To fully make use of our deep neural network, we partition the scene space into an adaptive hierarchical grid, in which we apply our network to reconstruct high-quality sampling distributions for any local region in the scene.
Neural Reflectance Fields for Appearance Acquisition
We combine this representation with a physically-based differentiable ray marching framework that can render images from a neural reflectance field under any viewpoint and light.
Deep Multi Depth Panoramas for View Synthesis
We propose a learning-based approach for novel view synthesis for multi-camera 360$^{\circ}$ panorama capture rigs.
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes.
Deep Reflectance Volumes: Relightable Reconstructions from Multi-View Photometric Images
We also show that our learned reflectance volumes are editable, allowing for modifying the materials of the captured scenes.
Deep Photon Mapping
This network is easy to incorporate in many previous photon mapping methods (by simply swapping the kernel density estimator) and can produce high-quality reconstructions of complex global illumination effects like caustics with an order of magnitude fewer photons compared to previous photon mapping methods.
Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images
We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting.
Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness
In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to the uncertainties of previous per-pixel depth predictions.
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3D Reconstruction
on DTU
Single Image Portrait Relighting
Lighting plays a central role in conveying the essence and depth of the subject in a portrait photograph.
Robust Energy Minimization for BRDF-Invariant Shape From Light Fields
On the other hand, recent works have explored PDE invariants for shape recovery with complex BRDFs, but they have not been incorporated into robust numerical optimization frameworks.
