WSCLoc: Weakly-Supervised Sparse-View Camera Relocalization via Radiance Field

Belangrijkste concepten

Introducing WSCLoc for enhanced weakly-supervised camera relocalization in sparse-view scenarios.

Samenvatting

The content discusses the challenges of obtaining ground truth pose labels for training deep learning-based camera relocalization models. It introduces WSCLoc, a system that customizes various models to improve performance under weakly-supervised and sparse view conditions. The system consists of two stages: WFT-NeRF for generating pose labels and WFT-Pose for accurate pose estimation. Experimental results on different datasets show superior accuracy in sparse-view scenarios compared to state-of-the-art methods.
I. Introduction

Challenges in camera relocalization with dense-view images.
Importance of weakly supervised methods.
Introduction of WSCLoc to address sparse-view scenarios.
II. Related Work

Overview of weakly supervised camera relocalization methods.
Advancements in camera pose estimation techniques.
Limitations and challenges faced by existing methods.
III. Methods
A. Weakly-supervised Free-Trajectory (WFT) NeRF

Explicit Scale Constraint to mitigate scale drift.
Explicit Time Encoding to handle image distortions.
B. Weakly-supervised Free-Trajectory Pose (WFT-Pose)

Inter-frame Geometric Constraints for accurate pose estimation.
Time-Encoding Based Random View Synthesis for data augmentation.
IV. Experiments
A. Relocalization Models and Evaluation Metrics

Application of WSCLoc to enhance DFNet and PoseNet models.
B. Datasets

Evaluation on 7-scenes and Cambridge Landmarks datasets.
C. Implementation Details

Training details and hyperparameters used in experiments.
D. WSCLoc Performance Evaluation

Comparison of WSCLoc with baseline models on different datasets.
V. Conclusions
Summary of the contributions and effectiveness of WSCLoc in improving weakly-supervised camera relocalization in sparse-view scenarios.

Statistieken

Despite the advancements in deep learning, obtaining ground truth pose labels remains costly.
WSCLoc enhances performance under weakly-supervised and sparse view conditions.

Citaten

"We propose a WFT-NeRF model that employs neural radiance techniques."
"Our experimental results demonstrate superior accuracy in sparse-view scenarios."

Belangrijkste Inzichten Gedestilleerd Uit

WSCLoc

by Jialu Wang,K... om arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15272.pdf

Diepere vragen

How can WSCLoc be adapted to other deep learning-based tasks beyond camera relocalization

WSCLoc can be adapted to other deep learning-based tasks beyond camera relocalization by leveraging its two-stage approach and key innovations. The initial stage involves generating pose labels through WFT-NeRF, which optimizes image reconstruction quality and initial pose information. This stage can be modified to suit different tasks by adjusting the network architecture and loss functions to align with the specific requirements of the new task. Additionally, in the second stage, WFT-Pose integrates pre-trained WFT-NeRF for accurate pose estimation. This co-optimization process can be applied to various deep learning models by incorporating inter-frame geometric constraints and Time-Encoding Based Random View Synthesis (TE-based RVS) tailored to the new task's needs.

What are potential drawbacks or limitations of relying on Structure-from-Motion techniques for label generation

Relying on Structure-from-Motion (SfM) techniques for label generation has some potential drawbacks or limitations. One limitation is that SfM may struggle in accurately providing pose labels under sparse-view scenarios due to issues like scale drift and image deformation. In such cases, noisy or inaccurate poses generated by SfM could lead to degraded performance in subsequent tasks like camera relocalization. Another drawback is that SfM typically requires dense-view images for effective label generation, making it computationally demanding and impractical for consumer-grade devices with resource constraints.

How might the concepts introduced by WSCLoc be applied to real-world applications outside of autonomous driving or AR

The concepts introduced by WSCLoc have broader applications beyond autonomous driving or augmented reality (AR). For instance:

Robotics: WSCLoc's weakly-supervised approach could be utilized in robot localization tasks where obtaining ground truth data is challenging.
Medical Imaging: The system's ability to enhance performance under weakly-supervised conditions could benefit medical imaging tasks requiring accurate spatial understanding.
Environmental Monitoring: WSCLoc's methods could be applied in environmental monitoring systems where precise location tracking is essential.
Industrial Automation: The techniques from WSCLoc could improve localization accuracy in industrial automation settings involving robots or drones operating within complex environments.
By adapting these concepts creatively, WSCLoc's innovations can find diverse applications across various domains requiring robust spatial understanding capabilities without heavy manual annotation efforts required traditionally.