Learning Multi-Scene Absolute Pose Regression with Transformers (2103.11468v2)

Abstract: Absolute camera pose regressors estimate the position and orientation of a camera from the captured image alone. Typically, a convolutional backbone with a multi-layer perceptron head is trained with images and pose labels to embed a single reference scene at a time. Recently, this scheme was extended for learning multiple scenes by replacing the MLP head with a set of fully connected layers. In this work, we propose to learn multi-scene absolute camera pose regression with Transformers, where encoders are used to aggregate activation maps with self-attention and decoders transform latent features and scenes encoding into candidate pose predictions. This mechanism allows our model to focus on general features that are informative for localization while embedding multiple scenes in parallel. We evaluate our method on commonly benchmarked indoor and outdoor datasets and show that it surpasses both multi-scene and state-of-the-art single-scene absolute pose regressors. We make our code publicly available from https://github.com/yolish/multi-scene-pose-transformer.

• The paper introduces a unified Transformer-based model for absolute pose regression that processes multiple scenes concurrently.
• It utilizes distinct Transformer encoders for positional and orientational features, significantly enhancing regression accuracy.
• The approach demonstrates robust performance, reducing errors on key benchmarks like Cambridge Landmarks and 7Scenes.

Learning Multi-Scene Absolute Pose Regression with Transformers

The paper "Learning Multi-Scene Absolute Pose Regression with Transformers" offers a novel approach to absolute camera pose regression (APR) by leveraging the strengths of Transformer architectures in dealing with multi-scene environments. Unlike traditional methods that require separate training for each scene, this work introduces a unified model that applies Transformers for parallel scene encoding. This innovation addresses the inherent limitations of single-scene APRs and offers enhanced scalability and generalization.

Methodological Innovations

The authors employ a Transformer-based architecture that harnesses both positional and orientational attention mechanisms. The model uniquely separates the processing of position- and orientation-informative features using two different Transformer encoders. The encoders apply self-attention to aggregate task-specific latent features from activation maps generated by a convolutional neural network (CNN) backbone, with decoders transforming these aggregated features into candidate pose predictions. This approach enables the model to effectively handle features that are critical to localization tasks while maintaining the capability to process multiple scenes concurrently.

A critical aspect of this methodology is the shift from traditional multi-layer perceptron (MLP) setups to Transformer mechanisms. The Transformer decoders output latent scene-specific embeddings, allowing the model to flexibly adapt to different scenes without requiring scene-specific MLP heads. As shown in their experiments, these innovations result in improved regression accuracy compared to existing APR models, both in single- and multi-scene scenarios.

Numerical Results and Comparisons

Extensive evaluations are conducted using standard benchmarks such as the Cambridge Landmarks and 7Scenes datasets. The paper demonstrates that the proposed method surpasses the current state-of-the-art models in both single- and multi-scene APR tasks. Specifically, the average position and orientation errors are significantly reduced on both datasets. For instance, on the Cambridge Landmarks dataset, their model achieves a mean orientation error of 2.73 degrees, which is an improvement over previous approaches. Additionally, the proposed model exhibits remarkable robustness and generalizes well across different environmental settings, as evidenced by consistent performance even with multi-dataset training.

Theoretical and Practical Implications

The move towards using Transformers in APR tasks represents a paradigm shift, offering a framework that can seamlessly incorporate the complexities of multiple scenes. The self-attention mechanism in Transformers supports more nuanced feature extraction and integration, addressing both positional and rotational aspects of pose estimation more effectively than previous methods that relied heavily on convolutional backbones alone.

Practically, this approach has significant implications for applications where large sets of environments need to be managed, such as autonomous vehicles or augmented reality. The model's capability to generalize across different datasets and scenes without necessitating separate models for each scene can lead to considerable resource savings in terms of computational cost and memory utilization. This is crucial for deploying APR systems in real-world scenarios where environments can vary greatly.

Future Developments in AI

This paper sets the stage for future work in enhancing the capabilities of APR models and potentially extending Transformer-based approaches to other areas of computer vision and robotics that require robust scene understanding and localization. As GPU processing capabilities improve and Transformer architectures are further optimized, models like this could benefit from even greater scalability and efficiency. Future research may also explore integrating additional sensory data, such as lidar or radar, into the Transformer framework to further improve localization accuracy and robustness.

In conclusion, the author's contribution represents an important step in advancing absolute pose regression, providing a cohesive and potentially more effective approach to handling diverse and complex scenes using modern Transformer architectures. The implications for widespread applicability and efficient deployment make this a noteworthy advancement in the field of computer vision.