Point-SLAM: Dense Neural Point Cloud-based SLAM (2304.04278v3)

Abstract: We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point cloud that is iteratively generated in an input-dependent data-driven manner. We demonstrate that both tracking and mapping can be performed with the same point-based neural scene representation by minimizing an RGBD-based re-rendering loss. In contrast to recent dense neural SLAM methods which anchor the scene features in a sparse grid, our point-based approach allows dynamically adapting the anchor point density to the information density of the input. This strategy reduces runtime and memory usage in regions with fewer details and dedicates higher point density to resolve fine details. Our approach performs either better or competitive to existing dense neural RGBD SLAM methods in tracking, mapping and rendering accuracy on the Replica, TUM-RGBD and ScanNet datasets. The source code is available at https://github.com/eriksandstroem/Point-SLAM.

• The paper introduces a novel dense point cloud SLAM that dynamically adapts point density from RGBD input for improved computational efficiency.
• The method minimizes an RGBD re-rendering loss, achieving up to an 85% reduction in depth L1 error and superior geometric reconstruction on benchmarks.
• The approach enhances real-time SLAM applications in AR, VR, robotics, and autonomous navigation while optimizing resource usage in dynamic environments.

Point-SLAM: Dense Neural Point Cloud-based SLAM

The paper "Point-SLAM: Dense Neural Point Cloud-based SLAM" introduces an innovative approach to simultaneous localization and mapping (SLAM), specifically focusing on the use of a dense neural point cloud for monocular RGBD input. This research presents a point-based alternative to traditional grid-based SLAM methods, aiming to optimize computational and memory efficiency while maintaining competitive accuracy in tracking and mapping tasks.

Methodology

Point-SLAM leverages a point cloud as the anchor for neural features, dynamically adapting the density of points according to the information content of the input data. This approach contrasts with traditional SLAM methods, which often fix feature points on static grids. By minimizing an RGBD-based re-rendering loss, both tracking and mapping are performed using a shared point-based neural representation. This enables efficient resource allocation, dedicating higher point density to areas with fine detail and reducing it in less complex regions.

Results

The paper reports substantial improvements in tracking and mapping accuracy across several benchmarks, including the Replica, TUM-RGBD, and ScanNet datasets. The proposed method achieves superior depth L1 error rates, indicating enhanced precision in reconstructed scenes. For example, Point-SLAM shows an 85% reduction in the depth L1 metric compared to existing methods. Furthermore, it outperforms competing SLAM frameworks in geometric reconstruction accuracy and rendering metrics like PSNR, SSIM, and LPIPS.

Implications and Future Directions

The adaptive resolution strategy adopted by Point-SLAM is pivotal for reducing computational overhead without sacrificing detail, demonstrating potential for real-time applications in augmented and virtual reality, robotics, and autonomous navigation. By focusing computational resources adaptively, Point-SLAM can achieve a balance between memory usage and accuracy, making it suitable for deployment in resource-constrained environments.

Theoretical implications of this work suggest that point-based neural representations could offer superior flexibility and scalability compared to traditional voxel-based models. Future research may explore optimization of point location updates at runtime, further enhancing adaptability to dynamic environments. Additionally, addressing challenges such as specularities and motion blur remains crucial for improving robustness across diverse real-world scenarios.

Point-SLAM's innovative use of a data-driven dynamic point cloud represents a significant shift towards more adaptable and resource-efficient SLAM methodologies, setting the stage for next-generation advances in neural scene representation.