RGB-D Mapping and Tracking in a Plenoxel Radiance Field (2307.03404v2)

Abstract: The widespread adoption of Neural Radiance Fields (NeRFs) have ensured significant advances in the domain of novel view synthesis in recent years. These models capture a volumetric radiance field of a scene, creating highly convincing, dense, photorealistic models through the use of simple, differentiable rendering equations. Despite their popularity, these algorithms suffer from severe ambiguities in visual data inherent to the RGB sensor, which means that although images generated with view synthesis can visually appear very believable, the underlying 3D model will often be wrong. This considerably limits the usefulness of these models in practical applications like Robotics and Extended Reality (XR), where an accurate dense 3D reconstruction otherwise would be of significant value. In this paper, we present the vital differences between view synthesis models and 3D reconstruction models. We also comment on why a depth sensor is essential for modeling accurate geometry in general outward-facing scenes using the current paradigm of novel view synthesis methods. Focusing on the structure-from-motion task, we practically demonstrate this need by extending the Plenoxel radiance field model: Presenting an analytical differential approach for dense mapping and tracking with radiance fields based on RGB-D data without a neural network. Our method achieves state-of-the-art results in both mapping and tracking tasks, while also being faster than competing neural network-based approaches. The code is available at: https://github.com/ysus33/RGB-D_Plenoxel_Mapping_Tracking.git

• The paper introduces a novel RGB-D integration into the Plenoxel model to overcome RGB-only limitations and improve 3D reconstruction accuracy.
• It employs an analytical, voxel-based approach using derivative equations for mapping and tracking without relying on neural networks.
• Experimental results on synthetic and real-world datasets show reduced depth errors and superior trajectory estimation compared to state-of-the-art methods.

Essay on "RGB-D Mapping and Tracking in a Plenoxel Radiance Field"

The paper "RGB-D Mapping and Tracking in a Plenoxel Radiance Field" addresses the limitations of current Neural Radiance Fields (NeRFs) for use in practical applications such as robotics and extended reality (XR). While NeRFs have gained popularity for novel view synthesis, they falter in creating accurate 3D reconstructions due to inherent ambiguities when using RGB data alone. This research highlights the necessity of incorporating depth data to overcome these limitations and proposes enhancements to the Plenoxel radiance field model to facilitate efficient, accurate RGB-D mapping and tracking without relying on neural networks.

Objectives and Contributions

The paper's primary objective is to enhance the Plenoxel model's capability by integrating RGB-D data, allowing the model to construct accurate 3D representations suitable for real-world applications. The authors argue that employing RGB-D sensors mitigates issues related to gradient-less regions in scenes, which are problematic when only using RGB data. The paper makes several contributions, including:

1. Differentiating models for novel view synthesis and models for 3D reconstruction, particularly highlighting their implications for robotics and XR.
2. Deriving analytical derivative equations necessary for mapping and tracking in voxel-based radiance fields based on RGB-D data.
3. Demonstrating improved performance in both mapping and tracking tasks compared to existing radiance field methods under equivalent computational constraints.

Methodology

The paper proposes an analytical approach to mapping and tracking that leverages the voxel grid representation of radiance fields. Unlike previous NeRF-based approaches that rely heavily on neural networks, the Plenoxel model adopts a voxel-based approach, representing the radiance field without neural networks. This approach significantly enhances processing speed while maintaining accuracy, as it allows direct data access and processing.

The mapping technique involves the optimization of radiance fields using specified photometric and geometric loss functions. Dense RGB and depth data guide this optimization. For tracking, the model estimates camera poses by aligning input RGB-D images with the learned model using volumetric rendering equations. This involves employing an image-to-model alignment strategy based on analytical derivatives of the model's rendering equations concerning camera poses.

Experimental Results

The proposed method was validated on both synthetic (Replica) and real-world (ScanNet) datasets, with a focus on achieving accurate mapping and real-time tracking. The results demonstrated that the incorporation of RGB-D data notably improved the model's geometric accuracy, with significant reductions in depth error compared to RGB-only models. When compared to state-of-the-art methods like NICE-SLAM and Vox-Fusion, the proposed method showed superior performance in trajectory estimation accuracy, as evidenced by lower Absolute Trajectory Errors (ATE) and Relative Pose Errors (RPE) for both translation and rotation metrics.

Future Implications and Speculation

This research underscores the importance of depth data for effective 3D mapping and tracking in NeRFs and related algorithms. The paper's findings have potential implications for the deployment of radiance field models in practical settings, such as autonomous navigation and XR environments, where accurate geometry is essential. Future developments in this area may focus on further optimization of computational efficiency and real-world adaptability, including dealing with dynamic scenes and increasing robustness against sensor noise. Additionally, the integration of advanced sensor fusion techniques might further enhance the model's capabilities, potentially paving the way for real-time applications on resource-constrained platforms.

In summary, this paper presents a compelling advancement in the domain of radiance field mapping and tracking, addressing foundational challenges and setting the stage for future research and practical implementations. The integration of RGB-D data, analytical derivatives, and voxel-based representation marks a significant step forward, offering a promising path for the deployment of radiance field techniques in complex, real-world applications.