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Found 21 papers, 11 papers with code
Single Mesh Diffusion Models with Field Latents for Texture Generation
We introduce a framework for intrinsic latent diffusion models operating directly on the surfaces of 3D shapes, with the goal of synthesizing high-quality textures.
Learning to Transform for Generalizable Instance-wise Invariance
However, too much or too little invariance can hurt, and the correct amount is unknown a priori and dependent on the instance.
Scaling Spherical CNNs
Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation.
LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
Consequently, there is growing interest in extending NeRF models to jointly optimize camera poses and scene representation, which offers an alternative to off-the-shelf SfM pipelines which have well-understood failure modes.
ASIC: Aligning Sparse in-the-wild Image Collections
We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection.
Stable Object Reorientation using Contact Plane Registration
We present a system for accurately predicting stable orientations for diverse rigid objects.
Generalizable Patch-Based Neural Rendering
Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably.
Light Field Neural Rendering
Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene.
Deep Semi-Supervised 3D Shape Reconstruction by Solving a Poisson Equation with Spectral Methods
In this paper we propose a deep learning method for unsupervised 3D implicit shape reconstruction from point clouds.
Generalized Fourier Features for Coordinate-Based Learning of Functions on Manifolds
In this work, we generalize positional encoding with Fourier features to non-Euclidean manifolds.
Implicit-PDF: Non-Parametric Representation of Probability Distributions on the Rotation Manifold
Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses.
Learning Equivariant Representations
In this thesis, we extend equivariance to other kinds of transformations, such as rotation and scaling.
An Analysis of SVD for Deep Rotation Estimation
Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto $O(n)$ or $SO(n)$.
Spin-Weighted Spherical CNNs
In this paper, we present a new type of spherical CNN that allows anisotropic filters in an efficient way, without ever leaving the spherical domain.
Ranked #20 on
Semantic Segmentation
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Theoretical Aspects of Group Equivariant Neural Networks
The second, by Cohen et al. (NeurIPS'19), generalizes the first to a larger class of networks, with feature maps as fields on homogeneous spaces.
Equivariant Multi-View Networks
Several popular approaches to 3D vision tasks process multiple views of the input independently with deep neural networks pre-trained on natural images, achieving view permutation invariance through a single round of pooling over all views.
Cross-Domain 3D Equivariant Image Embeddings
This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.
Labeling Panoramas with Spherical Hourglass Networks
With the recent proliferation of consumer-grade 360{\deg} cameras, it is worth revisiting visual perception challenges with spherical cameras given the potential benefit of their global field of view.
Learning SO(3) Equivariant Representations with Spherical CNNs
We address the problem of 3D rotation equivariance in convolutional neural networks.
Fast Multi-Image Matching via Density-Based Clustering
We consider the problem of finding consistent matches across multiple images.
Polar Transformer Networks
The result is a network invariant to translation and equivariant to both rotation and scale.
