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Found 13 papers, 10 papers with code
Learning a Diffusion Prior for NeRFs
Neural Radiance Fields (NeRFs) have emerged as a powerful neural 3D representation for objects and scenes derived from 2D data.
Polynomial Neural Fields for Subband Decomposition and Manipulation
We use this framework to design Fourier PNFs, which match state-of-the-art performance in signal representation tasks that use neural fields.
Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis
In this work, we address an important problem of optical see through (OST) augmented reality: non-negative image synthesis.
Stay Positive: Non-Negative Image Synthesis for Augmented Reality
In applications such as optical see-through and projector augmented reality, producing images amounts to solving non-negative image generation, where one can only add light to an existing image.
Geometry Processing with Neural Fields
Most existing geometry processing algorithms use meshes as the default shape representation.
Learning Gradient Fields for Shape Generation
Point cloud generation thus amounts to moving randomly sampled points to high-density areas.
QPyTorch: A Low-Precision Arithmetic Simulation Framework
Low-precision training reduces computational cost and produces efficient models.
PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flows
Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes and the second level is the distribution of points given a shape.
Ranked #3 on
Point Cloud Generation
on ShapeNet Car
SWALP : Stochastic Weight Averaging in Low-Precision Training
Low precision operations can provide scalability, memory savings, portability, and energy efficiency.
Deep Fundamental Matrix Estimation without Correspondences
Estimating fundamental matrices is a classic problem in computer vision.
Learning Single-View 3D Reconstruction with Limited Pose Supervision
It is expensive to label images with 3D structure or precise camera pose.
Learning to Evaluate Image Captioning
To address these two challenges, we propose a novel learning based discriminative evaluation metric that is directly trained to distinguish between human and machine-generated captions.
Fast Reading Comprehension with ConvNets
State-of-the-art deep reading comprehension models are dominated by recurrent neural nets.
