Instant 3D Scene Reconstruction with Microsoft HoloLens 2 Data using Depth-based 3D Gaussian Splatting

Core Concepts

HoloGS enables instant 3D scene reconstruction by leveraging the internal sensor data of the Microsoft HoloLens 2, including RGB images, camera poses, and depth information, to initialize and optimize 3D Gaussian Splatting.

Abstract

The paper presents HoloGS, a novel workflow for instant 3D scene reconstruction using 3D Gaussian Splatting with data from the Microsoft HoloLens 2. The key steps are:

Sensor Streaming: The HoloLens 2 server application is used to extract the required data, including RGB images, corresponding camera poses, and depth images.
Real-time Point Cloud Computation: The depth images are transformed into a 3D point cloud and merged into a joint point cloud using the camera poses.
Instant 3D Gaussian Splatting: The RGB images with corresponding camera poses and the point cloud from depth information are fed into the 3D Gaussian Splatting optimization process.

The authors investigate whether the data quality of the HoloLens is sufficient for 3D Gaussian Splatting by comparing the results to a traditional pipeline using external Structure from Motion (SfM) data. The analysis includes evaluating the training process, rendering quality, and geometric 3D accuracy of the densified point clouds extracted from the Gaussian centers.
The results show that HoloGS with internal HoloLens data can achieve reasonably smooth convergence of the 3D Gaussian Splatting optimization, reaching a maximum PSNR of around 20 dB. However, the external SfM data outperforms the internal HoloLens data in terms of rendering quality and geometric 3D accuracy. The authors discuss potential reasons for these discrepancies, such as the less precise camera poses of the RGB images in the internal HoloLens data, and propose strategies for further optimization, such as optimizing the camera poses during the training process.
Overall, the authors conclude that HoloGS represents a promising solution for using the Microsoft HoloLens 2 for instant 3D Gaussian Splatting, offering further research potential in the realm of photogrammetry, computer vision, and computer graphics.

Stats

The mean Chamfer Distance and standard deviation for the densified point clouds from Gaussian Splatting are:
For the 'Denker' scene:

External SfM data: mean 0.021, std 0.061
Internal HoloLens data: mean 0.298, std 0.534
For the 'Ficus' scene:

External SfM data: mean 0.045, std 0.261
Internal HoloLens data: mean 0.596, std 0.891

Quotes

"HoloGS enables instant 3D scene reconstruction by leveraging the internal sensor data of the Microsoft HoloLens 2, including RGB images, camera poses, and depth information, to initialize and optimize 3D Gaussian Splatting."
"The results show that HoloGS with internal HoloLens data can achieve reasonably smooth convergence of the 3D Gaussian Splatting optimization, reaching a maximum PSNR of around 20 dB."
"The authors discuss potential reasons for these discrepancies, such as the less precise camera poses of the RGB images in the internal HoloLens data, and propose strategies for further optimization, such as optimizing the camera poses during the training process."

Key Insights Distilled From

HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2

by Miri... at arxiv.org 05-06-2024

https://arxiv.org/pdf/2405.02005.pdf

Deeper Inquiries

How could the camera pose optimization during the training process improve the results of HoloGS with internal HoloLens data?

Camera pose optimization during the training process in HoloGS with internal HoloLens data could significantly enhance the results by improving the accuracy of the rendered images and the extracted densified point cloud. By refining the RGB camera poses during training, the system can better align the RGB images with the depth information, leading to sharper images and more precise point cloud reconstruction. This optimization can help reduce blurriness in the rendered images, minimize floater artifacts, and enhance the overall geometric accuracy of the 3D reconstruction. Additionally, optimizing camera poses can help address issues related to noisy edges, gaps in the point cloud, and inconsistencies in the geometry of the reconstructed scene. By ensuring that the camera poses are accurately calibrated and aligned with the depth data, the training process can converge more effectively, resulting in higher-quality outputs with improved visual fidelity.

How could the real-time capability of the HoloLens be leveraged to enable continuous updates and refinements to the 3D Gaussian Splatting during the optimization process, similar to SLAM approaches?

The real-time capability of the HoloLens can be leveraged to enable continuous updates and refinements to the 3D Gaussian Splatting during the optimization process by integrating feedback loops and iterative adjustments based on live sensor data. Similar to Simultaneous Localization and Mapping (SLAM) approaches, the HoloLens can provide instant feedback on the quality of the reconstructed scene, allowing for on-the-fly modifications to the Gaussian Splatting parameters. By continuously updating the Gaussian representations based on real-time sensor data, the system can adapt to changes in the environment, refine the geometry of the scene, and improve the accuracy of the rendered images and point cloud reconstruction. This dynamic optimization process can help address issues such as floater artifacts, noisy edges, and gaps in the point cloud by iteratively adjusting the Gaussian parameters to better fit the observed data. Additionally, real-time feedback can enable interactive adjustments during the reconstruction process, allowing for immediate corrections and enhancements to the 3D scene representation.

What other post-processing techniques could be explored to address the issues with floater artifacts and non-uniform point density in the densified point cloud extracted from the Gaussian centers?

To address the issues with floater artifacts and non-uniform point density in the densified point cloud extracted from the Gaussian centers, several post-processing techniques could be explored:

Density Moderation: Implementing a density moderation technique similar to Gaussian Splatting, where opacity values are adjusted to prevent the accumulation of floaters close to the camera poses. This can help reduce artifacts and improve the overall density distribution in the point cloud.

Surface Smoothing: Applying smoothing algorithms to the extracted point cloud to reduce noise and improve the surface continuity. Techniques like bilateral filtering or Laplacian smoothing can help enhance the geometric accuracy of the reconstructed scene.

Outlier Removal: Implementing outlier detection and removal algorithms to eliminate spurious points or artifacts in the point cloud. This can help clean up the data and improve the overall quality of the reconstructed geometry.

Point Cloud Refinement: Utilizing point cloud refinement techniques such as iterative closest point (ICP) or normal estimation to enhance the alignment and consistency of the point cloud data. These methods can help improve the overall shape and structure of the reconstructed scene.

By incorporating these post-processing techniques, the issues with floater artifacts and non-uniform point density in the densified point cloud can be mitigated, leading to more accurate and visually appealing 3D reconstructions.