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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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05/24/2023
On progressive sharpening, flat minima and generalisation
We present a new approach to understanding the relationship between loss...
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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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04/18/2023
Fast Neural Scene Flow
Scene flow is an important problem as it provides low-level motion cues ...
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04/04/2023
Re-Evaluating LiDAR Scene Flow for Autonomous Driving
Current methods for self-supervised LiDAR scene flow estimation work poo...
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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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10/10/2022
A global analysis of global optimisation
Theoretical understanding of the training of deep neural networks has ma...
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09/01/2022
On Quantizing Implicit Neural Representations
The role of quantization within implicit/coordinate neural networks is s...
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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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05/18/2022
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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03/29/2022
Long-term Visual Map Sparsification with Heterogeneous GNN
We address the problem of map sparsification for long-term visual locali...
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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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11/01/2021
Neural Scene Flow Prior
Before the deep learning revolution, many perception algorithms were bas...
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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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05/28/2021
On the Bias Against Inductive Biases
Borrowing from the transformer models that revolutionized the field of n...
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05/12/2021
Neural Trajectory Fields for Dynamic Novel View Synthesis
Recent approaches to render photorealistic views from a limited set of p...
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04/13/2021
BARF: Bundle-Adjusting Neural Radiance Fields
Neural Radiance Fields (NeRF) have recently gained a surge of interest w...
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03/31/2021
PAUL: Procrustean Autoencoder for Unsupervised Lifting
Recent success in casting Non-rigid Structure from Motion (NRSfM) as an ...
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03/10/2021
Reframing Neural Networks: Deep Structure in Overcomplete Representations
In comparison to classical shallow representation learning techniques, d...
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11/29/2020
Architectural Adversarial Robustness: The Case for Deep Pursuit
Despite their unmatched performance, deep neural networks remain suscept...
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10/31/2020
Scene Flow from Point Clouds with or without Learning
Scene flow is the three-dimensional (3D) motion field of a scene. It pro...
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10/20/2020
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Dense 3D object reconstruction from a single image has recently witnesse...
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10/19/2020
MaskNet: A Fully-Convolutional Network to Estimate Inlier Points
Point clouds have grown in importance in the way computers perceive the ...
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09/08/2020
Joint Pose and Shape Estimation of Vehicles from LiDAR Data
We address the problem of estimating the pose and shape of vehicles from...
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08/21/2020
Deterministic PointNetLK for Generalized Registration
There has been remarkable progress in the application of deep learning t...
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03/30/2020
Dataless Model Selection with the Deep Frame Potential
Choosing a deep neural network architecture is a fundamental problem in ...
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03/30/2020
When to Use Convolutional Neural Networks for Inverse Problems
Reconstruction tasks in computer vision aim fundamentally to recover an ...
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03/19/2020
High Accuracy Face Geometry Capture using a Smartphone Video
What's the most accurate 3D model of your face you can obtain while sitt...
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01/27/2020
Deep NRSfM++: Towards 3D Reconstruction in the Wild
The recovery of 3D shape and pose solely from 2D landmarks stemming from...
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12/12/2019
One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment
PointNet has recently emerged as a popular representation for unstructur...
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11/06/2019
Argoverse: 3D Tracking and Forecasting with Rich Maps
We present Argoverse – two datasets designed to support autonomous vehic...
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08/21/2019
PCRNet: Point Cloud Registration Network using PointNet Encoding
PointNet has recently emerged as a popular representation for unstructur...
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08/18/2019
Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning
We propose to learn a 3D pose estimator by distilling knowledge from Non...
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07/30/2019
Deep Non-Rigid Structure from Motion
Non-Rigid Structure from Motion (NRSfM) refers to the problem of reconst...
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05/07/2019
Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency
We present a learning based approach for multi-view stereopsis (MVS). Wh...
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04/25/2019
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes
We present a fully data-driven method to compute depth from diverse mono...
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03/20/2019
Photometric Mesh Optimization for Video-Aligned 3D Object Reconstruction
In this paper, we address the problem of 3D object mesh reconstruction f...
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03/13/2019
PointNetLK: Robust & Efficient Point Cloud Registration using PointNet
PointNet has revolutionized how we think about representing point clouds...
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02/28/2019
Deep Interpretable Non-Rigid Structure from Motion
All current non-rigid structure from motion (NRSfM) algorithms are limit...
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03/23/2018
Deep Convolutional Compressed Sensing for LiDAR Depth Completion
In this paper we consider the problem of estimating a dense depth map fr...
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03/22/2018
Aligning Across Large Gaps in Time
We present a method of temporally-invariant image registration for outdo...
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03/16/2018
Deep Component Analysis via Alternating Direction Neural Networks
Despite a lack of theoretical understanding, deep neural networks have a...
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03/05/2018
ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing
We address the problem of finding realistic geometric corrections to a f...
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12/07/2017
Take it in your stride: Do we need striding in CNNs?
Since their inception, CNNs have utilized some type of striding operator...
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12/07/2017
CNNs are Globally Optimal Given Multi-Layer Support
Stochastic Gradient Descent (SGD) is the central workhorse for training ...
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12/01/2017
