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Found 6 papers, 1 papers with code
$SE(3)$ Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation
Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning.
EqNIO: Subequivariant Neural Inertial Odometry
We then map the IMU data into this frame, thereby achieving an invariant canonicalization that can be directly used with off-the-shelf inertial odometry networks.
Equivariant Light Field Convolution and Transformer
We model the ray space, the domain of the light field, as a homogeneous space of $SE(3)$ and introduce the $SE(3)$-equivariant convolution in ray space.
Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces
We present a unified derivation of kernels via the Fourier domain by leveraging the sparsity of Fourier coefficients of the lifted feature fields.
Nested Scale Editing for Conditional Image Synthesis
We propose an image synthesis approach that provides stratified navigation in the latent code space.
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.
