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Found 20 papers, 3 papers with code
MeshLRM: Large Reconstruction Model for High-Quality Mesh
This allows for end-to-end mesh reconstruction by fine-tuning a pre-trained NeRF LRM with mesh rendering.
DATENeRF: Depth-Aware Text-based Editing of NeRFs
However, extending these techniques to edit scenes in Neural Radiance Fields (NeRF) is complex, as editing individual 2D frames can result in inconsistencies across multiple views.
PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction
We propose a Pose-Free Large Reconstruction Model (PF-LRM) for reconstructing a 3D object from a few unposed images even with little visual overlap, while simultaneously estimating the relative camera poses in ~1. 3 seconds on a single A100 GPU.
DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Model
We propose \textbf{DMV3D}, a novel 3D generation approach that uses a transformer-based 3D large reconstruction model to denoise multi-view diffusion.
Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model
Text-to-3D with diffusion models has achieved remarkable progress in recent years.
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.
PSDR-Room: Single Photo to Scene using Differentiable Rendering
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance.
I$^2$-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs
In this work, we present I$^2$-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields (SDFs).
I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs
Further, we propose to decompose the neural radiance field into spatially-varying material of the scene as a neural field through surface-based, differentiable Monte Carlo raytracing and emitter semantic segmentations, which enables physically based and photorealistic scene relighting and editing applications.
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields
Recent advances in neural radiance fields have enabled the high-fidelity 3D reconstruction of complex scenes for novel view synthesis.
Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing
Indoor scenes typically exhibit complex, spatially-varying appearance from global illumination, making inverse rendering a challenging ill-posed problem.
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.
IRON: Inverse Rendering by Optimizing Neural SDFs and Materials from Photometric Images
We propose a neural inverse rendering pipeline called IRON that operates on photometric images and outputs high-quality 3D content in the format of triangle meshes and material textures readily deployable in existing graphics pipelines.
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images.
Ranked #5 on
Surface Normals Estimation
on Stanford-ORB
Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering
Reconstructing the shape and appearance of real-world objects using measured 2D images has been a long-standing problem in computer vision.
Inverse Transport Networks
We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination.
Deep Painterly Harmonization
Copying an element from a photo and pasting it into a painting is a challenging task.
Graphics
Deep Photo Style Transfer
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
