23 citations · 35 across the 10 of their papers we have counts for
5 papers · 1 filter
PNeRF: Probabilistic Neural Scene Representations for Uncertain 3D Visual Mapping
Yassine Ahmine, Arnab Dey, Andrew I. Comport
Recently neural scene representations have provided very impressive results for representing 3D scenes visually, however, their study and progress have mainly been limited to visua…
Forecasting of depth and ego-motion with transformers and self-supervision
Houssem Boulahbal, Adrian Voicila, Andrew Comport
This paper addresses the problem of end-to-end self-supervised forecasting of depth and ego motion. Given a sequence of raw images, the aim is to forecast both the geometry and ego…
Mip-NeRF RGB-D: Depth Assisted Fast Neural Radiance Fields
Arnab Dey, Yassine Ahmine, Andrew I. Comport
Neural scene representations, such as Neural Radiance Fields (NeRF), are based on training a multilayer perceptron (MLP) using a set of color images with known poses. An increasing…
RGB-D Neural Radiance Fields: Local Sampling for Faster Training
Arnab Dey, Andrew I. Comport
Learning a 3D representation of a scene has been a challenging problem for decades in computer vision. Recent advances in implicit neural representation from images using neural ra…
Instance-aware multi-object self-supervision for monocular depth prediction
Houssem Boulahbal, Adrian Voicila, Andrew Comport
This paper proposes a self-supervised monocular image-to-depth prediction framework that is trained with an end-to-end photometric loss that handles not only 6-DOF camera motion bu…