5 citations · 7 across the 4 of their papers we have counts for
4 papers · 1 filter
STDepthFormer: Predicting Spatio-temporal Depth from Video with a Self-supervised Transformer Model
Houssem Boulahbal, Adrian Voicila, Andrew Comport
In this paper, a self-supervised model that simultaneously predicts a sequence of future frames from video-input with a novel spatial-temporal attention (ST) network is proposed. T…
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…
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…
Are conditional GANs explicitly conditional?
Houssem eddine Boulahbal, Adrian Voicila, Andrew Comport
This paper proposes two important contributions for conditional Generative Adversarial Networks (cGANs) to improve the wide variety of applications that exploit this architecture.…