# NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation

## Introduction

Contributions:

- A weakly-supervised object pose estimation that requires only 2D segmentation masks and relative camera poses
- OBJ-NeRF neural network encoding an implicit 3D object representation obtained from weak labels: segmentation masks and relative camera poses
- Pose regression network that relies on the above representation
- NeRF-enabled PnP + RANSAC approach for 6D pose estimation

They aim at densely generating accurate 2D-3D correspondences between input images and an implicit NeRF object representation obtained from weak labels.

As with NeRF training, they propose to regress poses of the NeRF reconstructed object with respect to some chosen reference frames.

## OBJ-NeRF

Estimate object pose in a camera coordinate system Loss:

- photometric loss (pixel diff) for the masked pixels
- mask loss (mask is predicted based on NeRF transmittance and alpha)
- reprojection error of the object center

With the learned canonical pose and neural model, the NOCS map can be rendered.

## Pose Estimation

They first use YOLOv3 to predict segmentation masks Then a pose regression network is trained on images to predict object coordinates $O$ and segmentation mask $M$. Finally their NeRF-enabled PnP+RANSAC improves the pose.

## Pose regression

the loss function consists of predicted mask error and NOCS error. NOCS loss contains

- average distance between predicted and ground-truth coordinates
- coordinate errors in first order ($L_grad$)
- coordinate errors in second order ($L_normal$)