Search Results for author:
Found 37 papers, 18 papers with code
Points2Surf Learning Implicit Surfaces from Point Clouds
Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans.
SuperGaussian: Repurposing Video Models for 3D Super Resolution
We demonstrate that it is possible to directly repurpose existing (pretrained) video models for 3D super-resolution and thus sidestep the problem of the shortage of large repositories of high-quality 3D training models.
TexSliders: Diffusion-Based Texture Editing in CLIP Space
Generative models have enabled intuitive image creation and manipulation using natural language.
PPSURF: Combining Patches and Point Convolutions for Detailed Surface Reconstruction
3D surface reconstruction from point clouds is a key step in areas such as content creation, archaeology, digital cultural heritage, and engineering.
Diffusion Handles Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Diffusion handles is a novel approach to enable 3D object edits on diffusion images requiring only existing pre-trained diffusion models depth estimation without any fine-tuning or 3D object retrieval.
Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D
Our key insight is to lift diffusion activations for an object to 3D using a proxy depth, 3D-transform the depth and associated activations, and project them back to image space.
Explorable Mesh Deformation Subspaces from Unstructured Generative Models
Deep generative models of 3D shapes often feature continuous latent spaces that can, in principle, be used to explore potential variations starting from a set of input shapes.
PhotoMat: A Material Generator Learned from Single Flash Photos
Instead, we train a generator for a neural material representation that is rendered with a learned relighting module to create arbitrarily lit RGB images; these are compared against real photos using a discriminator.
ShapeCoder: Discovering Abstractions for Visual Programs from Unstructured Primitives
The discovered abstractions capture common patterns (both structural and parametric) across the dataset, so that programs rewritten with these abstractions are more compact, and expose fewer degrees of freedom.
Unsupervised 3D Shape Reconstruction by Part Retrieval and Assembly
We instead propose to decompose shapes using a library of 3D parts provided by the user, giving full control over the choice of parts.
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models
We propose a diffusion model for neural implicit representations of 3D shapes that operates in the latent space of an auto-decoder.
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.
Search for Concepts: Discovering Visual Concepts Using Direct Optimization
Finding an unsupervised decomposition of an image into individual objects is a key step to leverage compositionality and to perform symbolic reasoning.
NeuForm: Adaptive Overfitting for Neural Shape Editing
Neural representations are popular for representing shapes, as they can be learned form sensor data and used for data cleanup, model completion, shape editing, and shape synthesis.
TileGen: Tileable, Controllable Material Generation and Capture
The resulting materials are tileable, can be larger than the target image, and are editable by varying the condition.
COFS: Controllable Furniture layout Synthesis
Scalable generation of furniture layouts is essential for many applications in virtual reality, augmented reality, game development and synthetic data generation.
Neural Convolutional Surfaces
Our pipeline and architecture are designed so that disentanglement of global geometry from local details is accomplished through optimization, in a completely unsupervised manner.
LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data
In practice, desirable layout properties may not exist in a dataset, for instance, specific expert knowledge can be missing in the data.
The Shape Part Slot Machine: Contact-based Reasoning for Generating 3D Shapes from Parts
We present the Shape Part Slot Machine, a new method for assembling novel 3D shapes from existing parts by performing contact-based reasoning.
Differentiable Surface Triangulation
Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.
SketchGen: Generating Constrained CAD Sketches
Sketches can be represented as graphs, with the primitives as nodes and the constraints as edges.
ShapeMOD: Macro Operation Discovery for 3D Shape Programs
In this paper, we present ShapeMOD, an algorithm for automatically discovering macros that are useful across large datasets of 3D shape programs.
Learning Delaunay Surface Elements for Mesh Reconstruction
We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.
Discovering Pattern Structure Using Differentiable Compositing
Patterns, which are collections of elements arranged in regular or near-regular arrangements, are an important graphic art form and widely used due to their elegant simplicity and aesthetic appeal.
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
The program captures the subset of variability that is interpretable and editable.
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views.
Points2Surf: Learning Implicit Surfaces from Point Cloud Patches
Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans.
StructEdit: Learning Structural Shape Variations
Learning to encode differences in the geometry and (topological) structure of the shapes of ordinary objects is key to generating semantically plausible variations of a given shape, transferring edits from one shape to another, and many other applications in 3D content creation.
StructureNet: Hierarchical Graph Networks for 3D Shape Generation
We introduce StructureNet, a hierarchical graph network which (i) can directly encode shapes represented as such n-ary graphs; (ii) can be robustly trained on large and complex shape families; and (iii) can be used to generate a great diversity of realistic structured shape geometries.
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers.
FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchronized GANs
The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods.
iMapper: Interaction-guided Joint Scene and Human Motion Mapping from Monocular Videos
A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video.
FrankenGAN: Guided Detail Synthesis for Building Mass-Models Using Style-Synchonized GANs
The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods.
Graphics
PCPNET: Learning Local Shape Properties from Raw Point Clouds
In this paper, we propose PCPNet, a deep-learning based approach for estimating local 3D shape properties in point clouds.
Computational Geometry
DepthCut: Improved Depth Edge Estimation Using Multiple Unreliable Channels
In the context of scene understanding, a variety of methods exists to estimate different information channels from mono or stereo images, including disparity, depth, and normals.
RAID: A Relation-Augmented Image Descriptor
As humans, we regularly interpret images based on the relations between image regions.
