Reprojection Errors as Prompts for Efficient Scene Coordinate Regression (2409.04178v1)

Abstract: Scene coordinate regression (SCR) methods have emerged as a promising area of research due to their potential for accurate visual localization. However, many existing SCR approaches train on samples from all image regions, including dynamic objects and texture-less areas. Utilizing these areas for optimization during training can potentially hamper the overall performance and efficiency of the model. In this study, we first perform an in-depth analysis to validate the adverse impacts of these areas. Drawing inspiration from our analysis, we then introduce an error-guided feature selection (EGFS) mechanism, in tandem with the use of the Segment Anything Model (SAM). This mechanism seeds low reprojection areas as prompts and expands them into error-guided masks, and then utilizes these masks to sample points and filter out problematic areas in an iterative manner. The experiments demonstrate that our method outperforms existing SCR approaches that do not rely on 3D information on the Cambridge Landmarks and Indoor6 datasets.

• The paper proposes Error-Guided Feature Selection (EGFS) that uses SAM to expand low-error regions, enhancing robust scene coordinate regression.
• It refines predictions with confidence maps and iterative updates, achieving state-of-the-art accuracy on outdoor and indoor datasets.
• The approach reduces computational demands and training time, offering practical benefits for AR, VR, and autonomous driving applications.

Overview of "Reprojection Errors as Prompts for Efficient Scene Coordinate Regression"

The paper "Reprojection Errors as Prompts for Efficient Scene Coordinate Regression" explores the field of visual localization, an essential component of applications such as Augmented Reality (AR), Virtual Reality (VR), and autonomous driving. It proposes an efficient strategy for Scene Coordinate Regression (SCR) by leveraging reprojection errors and employing the Segment Anything Model (SAM) to enhance training robustness against dynamic objects and texture-less regions.

Introduction and Background

Scene Coordinate Regression (SCR) has demonstrated significant promise in achieving accurate visual localization by establishing 2D-3D correspondences. The authors identify two primary challenges in SCR: dynamic objects and texture-less regions, which traditionally impair model performance. While traditional approaches like feature-matching and direct SCR methods offer solutions, they often fall short due to high computational demands, storage requirements, or inefficiencies in tackling dynamic elements and texture-less surfaces.

Methodology

The proposed methodology, Error-Guided Feature Selection (EGFS), is designed to address the aforementioned challenges. It consists of three main components:

1. Error-Guided Feature Selection with SAM: This component utilizes SAM to expand low reprojection-error areas into masks that can filter out problematic regions during training. The SAM model, specifically EfficientViT-SAM-L0, helps derive semantic masks from points with low reprojection errors. This ensures that the training process focuses on significant, lower-error areas without predefined semantic categories.
2. Scene Coordinate and EGFS Refinement with Confidence: The model incorporates a confidence map to refine the EGFS masks, ensuring that the training process emphasizes regions with reliable scene coordinate predictions. By predicting confidence scores and optimizing them alongside reprojection errors, the framework dynamically refines the selected regions throughout the training process.
3. Iterative Refinement Process: The training process is iterative, with EGFS masks generated every few epochs to dynamically update focused regions, enhancing model stability and performance over time.

Experimental Results

The authors conducted extensive evaluations on the Cambridge Landmarks and Indoor6 datasets, demonstrating the efficacy of their approach:

• Cambridge Landmarks:

The proposed EGFS method shows superior state-of-the-art (SOTA) performance compared to existing SCR approaches, including DSAC* and ACE, without relying on any 3D information. It achieves lower translational and rotational errors, indicating enhanced localization accuracy. Additionally, the models trained with EGFS are smaller and require less training time, exemplifying both efficiency and effectiveness.

• Indoor6:

On the Indoor6 dataset, EGFS maintains a competitive edge, outperforming DSAC* and ACE in terms of the proportion of translation and rotation errors below 5 cm/5°. These results further validate the robustness and generalizability of the proposed method across diverse environments.

Implications and Future Directions

The key contributions of this paper lie in its innovative use of reprojection errors and SAM for SCR. By effectively handling dynamic and texture-less regions, the proposed method significantly enhances visual localization accuracy and efficiency. The introduction of confidence maps and an iterative refinement process further solidifies its robustness.

Practically, this research paves the way for more reliable and efficient SCR methods applicable to AR, VR, and autonomous driving, where precise and rapid localization is critical. Theoretically, it underscores the importance of leveraging semantic information and confidence scores in optimizing SCR training processes.

Moving forward, exploring more advanced models for semantic expansion and refining the confidence thresholding method could yield even more robust results. Additionally, applying this approach to larger, more complex datasets may uncover further insights and improvements, pushing the boundaries of SCR methodologies.

In summary, this paper provides compelling evidence for the value of using reprojection errors as prompts for efficient SCR. The combination of SAM for semantic expansion and confidence-based refinement represents a significant step forward in addressing traditional SCR challenges, marking a meaningful contribution to the field of visual localization.