activity
20152024
most citedFlowNet: Learning Optical Flow with Convolutional Networks

604 citations · 830 across the 6 of their papers we have counts for

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12 papers · 1 filter

cs.CV20251 cited

E-3DGS: Event-Based Novel View Rendering of Large-Scale Scenes Using 3D Gaussian Splatting

Sohaib Zahid, Viktor Rudnev, Eddy Ilg +1

Novel view synthesis techniques predominantly utilize RGB cameras, inheriting their limitations such as the need for sufficient lighting, susceptibility to motion blur, and restric…

cs.CV2024

Spurfies: Sparse Surface Reconstruction using Local Geometry Priors

Kevin Raj, Christopher Wewer, Raza Yunus +2

We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synth…

cs.CV20221 cited

ERF: Explicit Radiance Field Reconstruction From Scratch

Samir Aroudj, Steven Lovegrove, Eddy Ilg +3

We propose a novel explicit dense 3D reconstruction approach that processes a set of images of a scene with sensor poses and calibrations and estimates a photo-real digital model.…

cs.CV2021

Analysis and Mitigations of Reverse Engineering Attacks on Local Feature Descriptors

Deeksha Dangwal, Vincent T. Lee, Hyo Jin Kim +9

As autonomous driving and augmented reality evolve, a practical concern is data privacy. In particular, these applications rely on localization based on user images. The widely ado…

cs.CV2020

Domain Adaptation of Learned Features for Visual Localization

Sungyong Baik, Hyo Jin Kim, Tianwei Shen +3

We tackle the problem of visual localization under changing conditions, such as time of day, weather, and seasons. Recent learned local features based on deep neural networks have…

cs.CV2020

Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction

Rohan Chabra, Jan Eric Lenssen, Eddy Ilg +4

Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a…