Search Results for author:
Found 16 papers, 11 papers with code
DAE-Net: Deforming Auto-Encoder for fine-grained shape co-segmentation
We present an unsupervised 3D shape co-segmentation method which learns a set of deformable part templates from a shape collection.
ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering
Furthermore, we showcase the ability of our method to learn geometric details and textures from shapes reconstructed from real-world photos.
A Review of Deep Learning-Powered Mesh Reconstruction Methods
With the recent advances in hardware and rendering techniques, 3D models have emerged everywhere in our life.
MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures
Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views.
Ranked #1 on
Novel View Synthesis
on Mip-NeRF 360
AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis
In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis.
Neural Dual Contouring
We introduce neural dual contouring (NDC), a new data-driven approach to mesh reconstruction based on dual contouring (DC).
Learning Mesh Representations via Binary Space Partitioning Tree Networks
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built over a set of planes, where the planes and convexes are both defined by learned network weights.
Neural Marching Cubes
To tackle these challenges, we re-cast MC from a deep learning perspective, by designing tessellation templates more apt at preserving geometric features, and learning the vertex positions and mesh topologies from training meshes, to account for contextual information from nearby cubes.
CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assembly
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies.
DECOR-GAN: 3D Shape Detailization by Conditional Refinement
During testing, a style code is fed into the generator to condition the refinement.
COALESCE: Component Assembly by Learning to Synthesize Connections
We introduce COALESCE, the first data-driven framework for component-based shape assembly which employs deep learning to synthesize part connections.
BSP-Net: Generating Compact Meshes via Binary Space Partitioning
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.
BAE-NET: Branched Autoencoder for Shape Co-Segmentation
The unsupervised BAE-NET is trained with a collection of un-segmented shapes, using a shape reconstruction loss, without any ground-truth labels.
LOGAN: Unpaired Shape Transform in Latent Overcomplete Space
Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes concatenate multi-scale shape features, resulting in an overcomplete representation.
Learning Implicit Fields for Generative Shape Modeling
We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes.
BSD-GAN: Branched Generative Adversarial Network for Scale-Disentangled Representation Learning and Image Synthesis
The key feature of BSD-GAN is that it is trained in multiple branches, progressively covering both the breadth and depth of the network, as resolutions of the training images increase to reveal finer-scale features.
