GHNeRF: Learning Generalizable Human Features with Efficient Neural Radiance Fields

no code implementations • 9 Apr 2024 • Arnab Dey, Di Yang, Rohith Agaram, Antitza Dantcheva, Andrew I. Comport, Srinath Sridhar, Jean Martinet

In this paper, we introduce a novel approach, termed GHNeRF, designed to address these limitations by learning 2D/3D joint locations of human subjects with NeRF representation.

PNeRF: Probabilistic Neural Scene Representations for Uncertain 3D Visual Mapping

no code implementations • 23 Sep 2022 • Yassine Ahmine, Arnab Dey, Andrew I. Comport

The aim of this paper is therefore to propose a novel method for training {\em probabilistic neural scene representations} with uncertain training data that could enable the inclusion of these representations in robotics applications.

Mip-NeRF RGB-D: Depth Assisted Fast Neural Radiance Fields

no code implementations • 19 May 2022 • 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.

RGB-D Neural Radiance Fields: Local Sampling for Faster Training

no code implementations • 26 Mar 2022 • Arnab Dey, Andrew I. Comport

Learning a 3D representation of a scene has been a challenging problem for decades in computer vision.