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NeRF-Supervised Feature Point Detection and Description (2403.08156v3)

Published 13 Mar 2024 in cs.CV

Abstract: Feature point detection and description is the backbone for various computer vision applications, such as Structure-from-Motion, visual SLAM, and visual place recognition. While learning-based methods have surpassed traditional handcrafted techniques, their training often relies on simplistic homography-based simulations of multi-view perspectives, limiting model generalisability. This paper presents a novel approach leveraging Neural Radiance Fields (NeRFs) to generate a diverse and realistic dataset consisting of indoor and outdoor scenes. Our proposed methodology adapts state-of-the-art feature detectors and descriptors for training on multi-view NeRF-synthesised data, with supervision achieved through perspective projective geometry. Experiments demonstrate that the proposed methodology achieves competitive or superior performance on standard benchmarks for relative pose estimation, point cloud registration, and homography estimation while requiring significantly less training data and time compared to existing approaches.

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Summary

  • The paper presents a novel NeRF-supervised approach that generates a dataset of 10,000 synthesized images for robust feature point detection and description.
  • It employs both end-to-end and projective adaptation training methodologies using NeRF re-projection error to improve model accuracy.
  • Results demonstrate superior performance in relative pose estimation benchmarks, reducing reliance on large-scale traditional datasets.

Insights into NeRF-Supervised Feature Point Detection and Description

The paper entitled "NeRF-Supervised Feature Point Detection and Description" presents a methodology to enhance feature point detection and description by utilizing neural radiance fields (NeRFs) for generating multi-view training data. Feature point detection and description is crucial for computer vision applications such as Structure-from-Motion, visual SLAM, and visual place recognition. Traditional handcrafted techniques have largely been overtaken by learning-based approaches that typically rely on homography-based simulations for multi-view perspectives. However, the generation of simplistic homography warps limits the generalizability of these models. This paper introduces a novel method where NeRFs are employed to create a more realistic multi-view dataset, thereby improving model performance with less training data.

Core Contributions

The authors make the following significant contributions:

  1. Dataset Generation with NeRF: A new dataset comprising of 10,000 NeRF-synthesized images from 10 different indoor and outdoor scenes is created. This dataset includes corresponding depth maps and intrinsic and extrinsic parameters, providing a comprehensive multi-view training set for feature point models.
  2. NeRF-Based Training Methodology: Two training methodologies, namely end-to-end and projective adaptation, are proposed to leverage the NeRF-generated dataset. These methodologies use NeRF's re-projection error to supervise the training of feature point detection and description models.
  3. Evaluation and Comparative Results: The adapted versions of SuperPoint and SiLK, trained on NeRF-derived data, are compared against original baselines trained on significantly larger datasets such as MS-COCO. The NeRF-supervised models demonstrated superior performance in certain benchmarks, specifically in relative pose estimation in ScanNet and YFCC100M datasets, while displaying only slight underperformance on the HPatches homography estimation benchmark.

Implications and Future Directions

The application of NeRF in generating multi-view datasets introduces significant improvements in the quality of supervision for learning-based feature point detection and description. One of the primary advantages of this approach is the reduction in required training data size. The fact that a dataset considerably smaller than traditional alternatives can achieve competitive results underscores the potential efficiency gain using NeRF-synthesized views.

The findings suggest a potential paradigm shift in dataset generation for training computer vision models, shifting from large, manually collected datasets to more synthesized, efficient NeRF datasets. However, the development of NeRF itself is still evolving, with continual improvements in quality and efficiency anticipated. Enhanced NeRF outputs could further minimize current limitations related to depth precision and view synthesis under geometric constraints.

Looking forward, the advancement in neural rendering techniques can provide higher-quality synthetic images without artifacts and with precise depth maps, enabling more robust training data for feature detection and description. This can significantly propel the accuracy and generalization of computer vision applications in diverse environments and conditions.

The research discussed in this paper indicates a promising direction for training learning-based detectors and descriptors, moving away from the limitations of homography-based data towards leveraging the more realistic and versatile data generated by neural radiance fields. Such approaches could potentially redefine training paradigms for tasks that rely on robust and generalizable feature detection and description.

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