Search Results for author:
Found 34 papers, 18 papers with code
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
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.
Points2Surf Learning Implicit Surfaces from Point Clouds
Hence, deep learning based methods have recently been proposed to produce complete surfaces, even from partial scans.
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.
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-Synchonized GANs
The various GANs are synchronized to produce consistent style distributions over buildings and neighborhoods.
Graphics
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.
Differentiable Surface Triangulation
Our method builds on the result that any 2D triangulation can be achieved by a suitably perturbed weighted Delaunay triangulation.
Learning Delaunay Surface Elements for Mesh Reconstruction
We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements.
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.
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.
ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis
The program captures the subset of variability that is interpretable and editable.
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.
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.
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.
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.
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.
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.
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.
SketchGen: Generating Constrained CAD Sketches
Sketches can be represented as graphs, with the primitives as nodes and the constraints as edges.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
