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Found 28 papers, 14 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.
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.
Approximate Bijective Correspondence for isolating factors of variation
We propose a novel algorithm that relies on a weak form of supervision where the data is partitioned into sets according to certain \textit{inactive} factors of variation.
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.
Wide-Baseline Relative Camera Pose Estimation with Directional Learning
Modern deep learning techniques that regress the relative camera pose between two images have difficulty dealing with challenging scenarios, such as large camera motions resulting in occlusions and significant changes in perspective that leave little overlap between images.
KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control
We cast this as the problem of aligning a source 3D object to a target 3D object from the same object category.
De-rendering the World's Revolutionary Artefacts
Recent works have shown exciting results in unsupervised image de-rendering -- learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision.
Learning ABCs: Approximate Bijective Correspondence for isolating factors of variation with weak supervision
We propose a novel algorithm that utilizes a weak form of supervision where the data is partitioned into sets according to certain inactive (common) factors of variation which are invariant across elements of each set.
Leveraging affinity cycle consistency to isolate factors of variation in learned representations
In this work, we operate in the setting where limited information is known about the data in the form of groupings, or set membership, and the task is to learn representations which isolate the factors of variation that are common across the groupings.
Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image.
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
on Stanford2D3D Panoramic
Time Dependence in Non-Autonomous Neural ODEs
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation.
Local Implicit Grid Representations for 3D Scenes
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
Latent feature disentanglement for 3D meshes
Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR.
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.
Deformable Shape Completion with Graph Convolutional Autoencoders
In this work, we propose a novel learning-based method for the completion of partial shapes.
Learning SO(3) Equivariant Representations with Spherical CNNs
We address the problem of 3D rotation equivariance in convolutional neural networks.
Geometry of 3D Environments and Sum of Squares Polynomials
Motivated by applications in robotics and computer vision, we study problems related to spatial reasoning of a 3D environment using sublevel sets of polynomials.
Co-Segmentation of Textured 3D Shapes with Sparse Annotations
We present a novel co-segmentation method for textured 3D shapes.
