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09/01/2023
Robust Point Cloud Processing through Positional Embedding
End-to-end trained per-point embeddings are an essential ingredient of a...
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05/15/2023
Curvature-Aware Training for Coordinate Networks
Coordinate networks are widely used in computer vision due to their abil...
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03/10/2023
On the effectiveness of neural priors in modeling dynamical systems
Modelling dynamical systems is an integral component for understanding t...
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02/27/2023
BaLi-RF: Bandlimited Radiance Fields for Dynamic Scene Modeling
Reasoning the 3D structure of a non-rigid dynamic scene from a single mo...
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06/17/2022
How You Start Matters for Generalization
Characterizing the remarkable generalization properties of over-paramete...
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05/30/2022
Few-shot Class-incremental Learning for 3D Point Cloud Objects
Few-shot class-incremental learning (FSCIL) aims to incrementally fine-t...
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05/18/2022
Trading Positional Complexity vs. Deepness in Coordinate Networks
It is well noted that coordinate-based MLPs benefit – in terms of preser...
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04/12/2022
GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation
Despite Neural Radiance Fields (NeRF) showing compelling results in phot...
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02/01/2022
On Regularizing Coordinate-MLPs
We show that typical implicit regularization assumptions for deep neural...
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12/21/2021
Learning Positional Embeddings for Coordinate-MLPs
We propose a novel method to enhance the performance of coordinate-MLPs ...
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11/30/2021
Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPs
Coordinate-MLPs are emerging as an effective tool for modeling multidime...
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11/16/2021
Enabling equivariance for arbitrary Lie groups
Although provably robust to translational perturbations, convolutional n...
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07/06/2021
Rethinking Positional Encoding
It is well noted that coordinate based MLPs benefit greatly – in terms o...
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02/06/2021
Robust normalizing flows using Bernstein-type polynomials
Normalizing flows (NFs) are a class of generative models that allows exa...
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11/25/2020
How to train your conditional GAN: An approach using geometrically structured latent manifolds
Conditional generative modeling typically requires capturing one-to-many...
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10/07/2020
Conditional Generative Modeling via Learning the Latent Space
Although deep learning has achieved appealing results on several machine...
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12/04/2019
Spectral-GANs for High-Resolution 3D Point-cloud Generation
Point-clouds are a popular choice for vision and graphics tasks due to t...
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11/30/2019
Representation Learning on Unit Ball with 3D Roto-Translational Equivariance
Convolution is an integral operation that defines how the shape of one f...
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08/24/2019
Blended Convolution and Synthesis for Efficient Discrimination of 3D Shapes
Existing networks directly learn feature representations on 3D point clo...
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01/03/2019
Volumetric Convolution: Automatic Representation Learning in Unit Ball
Convolution is an efficient technique to obtain abstract feature represe...
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10/16/2018
Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos
Activity recognition in videos in a deep-learning setting---or otherwise...
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10/15/2018