Accurate and Scalable 3D Mapping with Normal-Guided Neural Non-Projective Signed Distance Fields

Główne pojęcia

A novel approach for large-scale 3D mapping using normal-guided neural non-projective signed distance fields, which achieves high-quality and efficient reconstruction by directly sampling accurate distance values along surface normals.

Streszczenie

The paper presents N3-Mapping, a framework for large-scale and high-quality 3D mapping using normal-guided neural non-projective signed distance fields (SDFs). The key contributions are:

1. A normal-guided sampling method to obtain accurate non-projective SDF labels for training the implicit neural map, which mitigates the approximation errors associated with using projective distances.

2. A voxel-oriented training strategy combined with a sliding window mechanism to alleviate the forgetting issue and maintain a bounded memory footprint during incremental mapping.

3. A hierarchical sampling approach to improve training efficiency by balancing the sampling across densely and sparsely observed regions.

The proposed method is evaluated on both simulated and real-world datasets, demonstrating state-of-the-art mapping accuracy and completeness compared to existing TSDF-based and implicit representation-based approaches. The normal-guided sampling, voxel-oriented training, and hierarchical sampling are shown to be effective through detailed ablation studies. The scalability and robustness of N3-Mapping are also validated on large-scale outdoor environments and challenging indoor scenes.

Statystyki

The average per-frame time consumption for the Maicity-01 dataset is 2.09 seconds, and for the KITTI-07 dataset is 2.45 seconds.
The memory consumption of historical data remains stable at around 1GB throughout the mapping process using the voxel-oriented sliding window strategy.

Cytaty

"Our method directly samples points and corresponding distance values along the normal direction near the surface. Such sampled SDF labels tend to be close to the ground truth, leading to improved mapping quality."
"We employ a voxel-oriented training strategy combined with a sliding window mechanism to alleviate the forgetting issue and enhance training efficiency."
"We propose a hierarchical sampling strategy to avoid redundant training in densely observed regions and insufficient training in sparsely observed regions."

Kluczowe wnioski z

N$^{3}$-Mapping: Normal Guided Neural Non-Projective Signed Distance Fields for Large-scale 3D Mapping

by Shuangfu Son... o arxiv.org 04-30-2024

https://arxiv.org/pdf/2401.03412.pdf

Głębsze pytania

How can the normal-guided sampling be further improved to handle more complex geometric structures and noise in the input data?

In order to enhance the normal-guided sampling technique for handling complex geometric structures and noisy input data, several strategies can be implemented:

Adaptive Sampling Density: Implementing an adaptive sampling density based on the local curvature of the surface can help in capturing intricate details of complex geometries. By increasing the sampling density in regions with high curvature, the method can better represent the surface.

Noise Filtering: Incorporating noise filtering techniques, such as outlier removal or denoising algorithms, before sampling along the normals can help in reducing the impact of noise on the sampled distance values. This can improve the accuracy of the signed distance field estimation.

Multi-Scale Sampling: Utilizing a multi-scale sampling approach where points are sampled at different scales along the normal direction can provide a more comprehensive representation of the surface geometry. This can help in capturing both fine details and overall shape characteristics.

Surface Feature Detection: Integrating surface feature detection algorithms to identify key geometric features, such as edges or corners, and adapt the sampling strategy accordingly. This can ensure that important structural elements are accurately represented in the signed distance field.

Robust Truncation Handling: Implementing robust truncation handling mechanisms to address the challenges posed by complex structures and noisy data. By dynamically adjusting the truncation interval based on local surface properties, the method can better handle varying geometric complexities.

How can the potential limitations of the voxel-oriented training strategy be addressed, and how can it be extended to handle dynamic environments or enable real-time performance?

To address the potential limitations of the voxel-oriented training strategy and extend its applicability to dynamic environments or real-time performance, the following approaches can be considered:

Dynamic Voxel Adaptation: Implementing a dynamic voxel adaptation mechanism that adjusts the voxel size or resolution based on the local scene complexity or motion dynamics can enhance the method's adaptability to dynamic environments. This can help in capturing both static and moving objects effectively.

Temporal Fusion: Introducing temporal fusion techniques to incorporate information from previous frames and maintain consistency in the mapping process. By fusing information over time, the method can handle dynamic changes in the environment and ensure continuous mapping updates.

Efficient Memory Management: Optimizing memory management strategies to efficiently store and update historical data within the sliding window. Implementing memory-efficient data structures and compression techniques can reduce memory overhead and enable real-time performance in large-scale environments.

Parallel Processing: Leveraging parallel processing capabilities to accelerate voxel-oriented training and hierarchical sampling. Distributing computation tasks across multiple cores or GPUs can speed up the mapping process and improve efficiency in handling dynamic scenes.

Adaptive Sampling Policies: Introducing adaptive sampling policies that prioritize regions with high information gain or dynamic changes can enhance the method's responsiveness to real-time updates. By dynamically adjusting the sampling strategy, the method can focus on relevant areas for accurate mapping.

Could the proposed techniques be applied to other tasks beyond 3D mapping, such as object reconstruction or scene understanding?

Yes, the techniques proposed in the context of 3D mapping, such as normal-guided sampling, voxel-oriented training, and implicit neural representations, can be extended to various other tasks beyond mapping. Some potential applications include:

Object Reconstruction: The normal-guided sampling approach can be utilized for reconstructing detailed 3D models of objects from sparse or noisy point cloud data. By leveraging implicit neural representations, accurate object reconstructions can be achieved with enhanced surface details.

Scene Understanding: The voxel-oriented training strategy can be applied to scene understanding tasks, such as semantic segmentation or scene classification. By encoding scene information in hierarchical voxel structures, the method can facilitate comprehensive scene analysis and interpretation.

Robot Localization: The techniques can be adapted for robot localization tasks by incorporating implicit neural representations for environment mapping and localization. By integrating normal-guided sampling for accurate distance estimation, robots can navigate and localize themselves in complex environments effectively.

Augmented Reality: The methods can be employed in augmented reality applications for real-time scene reconstruction and interaction. By leveraging voxel-based representations and efficient training strategies, immersive AR experiences with realistic virtual objects can be created.

Medical Imaging: The techniques can also be applied to medical imaging tasks, such as organ reconstruction or tumor segmentation. By utilizing implicit neural representations and adaptive sampling, detailed 3D models of anatomical structures can be generated for diagnostic purposes.