RegFormer: Efficient Transformer Network for Large-Scale Point Cloud Registration

RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration

This document details the research paper "RegFormer: An Efficient Projection-Aware Transformer Network for Large-Scale Point Cloud Registration" by Jiuming Liu, Guangming Wang, Zhe Liu, Chaokang Jiang, Marc Pollefeys, and Hesheng Wang, presented at ICCV 2023.

Introduction

Point cloud registration, the process of aligning multiple 3D point clouds, has seen significant advancements, particularly for object-level and indoor scene applications. However, large-scale registration, especially for outdoor LiDAR scans, remains a challenging area. The difficulties stem from the sheer volume of points, their complex distributions, and the presence of numerous outliers in these datasets.

Traditional methods often employ a two-stage approach: first, extracting discriminative local features to find correspondences, and second, using estimators like RANSAC to filter outliers. This pipeline is heavily reliant on the quality of hand-crafted descriptors and the effectiveness of post-processing steps.

To overcome these limitations, the paper introduces RegFormer, an end-to-end transformer network designed for efficient and accurate large-scale point cloud alignment without the need for post-processing.

Challenges in Large-Scale Point Cloud Registration

Massive Point Density: Outdoor LiDAR scans can contain millions or even billions of points, making processing computationally intensive.
Complex Distributions: The spatial arrangement of points can be highly irregular, influenced by environmental factors and sensor characteristics.
Outlier Presence: Noise, occlusions, and sensor errors introduce a significant number of outliers that can corrupt the registration process.
Descriptor Sensitivity: Existing methods often depend on robust local feature descriptors, which can be difficult to design and may not generalize well across different scenes or sensors.
Two-Stage Pipeline Limitations: The reliance on separate feature extraction and outlier rejection stages can lead to error propagation and inefficiencies.

RegFormer: The Proposed Solution

RegFormer addresses these challenges through a novel, end-to-end transformer-based architecture.

1. Projection-Aware Hierarchical Transformer

Global Feature Extraction: This component is designed to capture long-range dependencies between points in the cloud. By considering the global context, it can more effectively identify and filter outliers.
Hierarchical Structure: The transformer operates hierarchically, allowing it to process the point cloud at different levels of detail, which is crucial for handling varying densities and scales.
Linear Complexity: A key innovation is the transformer's linear complexity with respect to the number of points. This ensures that RegFormer remains efficient even when dealing with very large-scale point clouds, a significant improvement over quadratic or higher-order complexities of some previous methods.

2. Bijective Association Transformer

Initial Transformation Regression: This module is responsible for regressing the initial transformation (rotation and translation) between two point clouds.
Reducing Mismatches: By employing a bijective association mechanism, the transformer aims to minimize incorrect correspondences, leading to a more accurate initial alignment.

Key Contributions

End-to-End Registration: RegFormer offers a unified framework for point cloud registration, eliminating the need for separate feature extraction and outlier rejection modules.
Projection-Awareness: The network leverages projection information to enhance feature learning and outlier filtering.
Hierarchical Transformer: A novel hierarchical transformer architecture captures multi-scale contextual information.
Linear Complexity: The design ensures computational efficiency for large-scale datasets.
Bijective Association: An effective mechanism for robust initial transformation estimation.

Experimental Results

The effectiveness of RegFormer was validated through extensive experiments on the KITTI and NuScenes datasets, which are standard benchmarks for large-scale outdoor scene understanding and autonomous driving.

Competitive Performance: RegFormer achieved competitive results in terms of both accuracy and efficiency compared to state-of-the-art methods.
Efficiency Gains: The linear complexity of the transformer contributed to significant speedups, making it practical for real-world applications.

Availability

The source code for RegFormer is publicly available on GitHub at https://github.com/IRMVLab/RegFormer.

Research Areas

The research falls under the following areas:

Artificial Intelligence
Computer Vision

Research Labs

The work was conducted at:

Spatial AI Lab – Zurich

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Conclusion

RegFormer represents a significant step forward in large-scale point cloud registration by offering an efficient, end-to-end solution that leverages the power of transformer networks. Its projection-aware and hierarchical design, coupled with linear complexity, makes it a promising tool for various applications in robotics, autonomous driving, and 3D scene reconstruction.