Search Results for author:
Found 15 papers, 7 papers with code
CSGNet: Neural Shape Parser for Constructive Solid Geometry
In contrast, our model uses a recurrent neural network that parses the input shape in a top-down manner, which is significantly faster and yields a compact and easy-to-interpret sequence of modeling instructions.
Search-Guided, Lightly-supervised Training of Structured Prediction Energy Networks
In this paper, we instead use efficient truncated randomized search in this reward function to train structured prediction energy networks (SPENs), which provide efficient test-time inference using gradient-based search on a smooth, learned representation of the score landscape, and have previously yielded state-of-the-art results in structured prediction.
Learning Point Embeddings from Shape Repositories for Few-Shot Segmentation
We present a framework for learning representations of 3D shapes that reflect the information present in this meta data and show that it leads to improved generalization for semantic segmentation tasks.
Neural Shape Parsers for Constructive Solid Geometry
We investigate two architectures for this task --- a vanilla encoder (CNN) - decoder (RNN) and another architecture that augments the encoder with an explicit memory module based on the program execution stack.
ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds
We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives.
Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions
The problems of shape classification and part segmentation from 3D point clouds have garnered increasing attention in the last few years.
PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentation
We present PriFit, a semi-supervised approach for label-efficient learning of 3D point cloud segmentation networks.
MvDeCor: Multi-view Dense Correspondence Learning for Fine-grained 3D Segmentation
As a result, the learned 2D representations are view-invariant and geometrically consistent, leading to better generalization when trained on a limited number of labeled shapes compared to alternatives that utilize self-supervision in 2D or 3D alone.
Attention Beats Concatenation for Conditioning Neural Fields
Neural fields model signals by mapping coordinate inputs to sampled values.
Unsupervised Semantic Correspondence Using Stable Diffusion
Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images.
Ranked #1 on
Semantic correspondence
on PF-WILLOW
Accelerating Neural Field Training via Soft Mining
We present an approach to accelerate Neural Field training by efficiently selecting sampling locations.
Unsupervised Keypoints from Pretrained Diffusion Models
Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures, but performance is yet to match the supervised counterpart, making their practicability questionable.
Ranked #1 on
Unsupervised Human Pose Estimation
on Tai-Chi-HD
Volumetric Rendering with Baked Quadrature Fields
We propose a novel Neural Radiance Field (NeRF) representation for non-opaque scenes that allows fast inference by utilizing textured polygons.
PointNeRF++: A multi-scale, point-based Neural Radiance Field
We overcome these problems with a simple representation that aggregates point clouds at multiple scale levels with sparse voxel grids at different resolutions.
3D Gaussian Splatting as Markov Chain Monte Carlo
While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which does not always generalize and may lead to poor-quality renderings.
