Core Concepts
An efficient NeRF-based pose estimation method is proposed that combines image matching and NeRF to directly solve the pose in one step, avoiding the need for hundreds of optimization steps and overcoming issues with local minima.
Abstract
The paper proposes an efficient NeRF-based pose estimation method that combines image matching with NeRF to directly solve the pose in one step, without requiring hundreds of optimization steps.
Key highlights:
The method marries image matching with NeRF to build 2D-3D correspondences and directly solve the pose via PnP, significantly reducing the number of iterations compared to previous NeRF-based methods.
A 3D consistent point mining strategy is introduced to detect and discard unfaithful 3D points reconstructed by NeRF, improving the accuracy of the 2D-3D correspondences.
A keypoint-guided occlusion robust refinement strategy is proposed to handle occluded images, which current NeRF-based methods struggle with.
Experiments show the proposed method outperforms state-of-the-art NeRF-based and image matching-based methods, achieving real-time pose estimation at 6 FPS.
Stats
Our method improves the inference efficiency over former NeRF based methods by 90 times.
Our method achieves real-time pose estimation at 6 FPS.
Quotes
Our method only requires 40 steps of optimization, much less than iNeRF (300 steps) and pi-NeRF (2500 steps).