23 citations · 35 across the 10 of their papers we have counts for
10 papers
HFGaussian: Learning Generalizable Gaussian Human with Integrated Human Features
Arnab Dey, Cheng-You Lu, Andrew I. Comport +3
Recent advancements in radiance field rendering show promising results in 3D scene representation, where Gaussian splatting-based techniques emerge as state-of-the-art due to their…
GHNeRF: Learning Generalizable Human Features with Efficient Neural Radiance Fields
Arnab Dey, Di Yang, Rohith Agaram +4
Recent advances in Neural Radiance Fields (NeRF) have demonstrated promising results in 3D scene representations, including 3D human representations. However, these representations…
DiVa-360: The Dynamic Visual Dataset for Immersive Neural Fields
Cheng-You Lu, Peisen Zhou, Angela Xing +8
Advances in neural fields are enabling high-fidelity capture of the shape and appearance of dynamic 3D scenes. However, their capabilities lag behind those offered by conventional…
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…
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…
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.…