LMSCNet: Lightweight Multiscale 3D Semantic Completion (2008.10559v2)

Abstract: We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized sparse 3D LiDAR scans. As opposed to the literature, we use a 2D UNet backbone with comprehensive multiscale skip connections to enhance feature flow, along with 3D segmentation heads. On the SemanticKITTI benchmark, our method performs on par on semantic completion and better on occupancy completion than all other published methods -- while being significantly lighter and faster. As such it provides a great performance/speed trade-off for mobile-robotics applications. The ablation studies demonstrate our method is robust to lower density inputs, and that it enables very high speed semantic completion at the coarsest level. Our code is available at https://github.com/cv-rits/LMSCNet.

• The paper presents a novel lightweight architecture that integrates a 2D UNet backbone with multiscale skip connections for efficient 3D semantic scene completion.
• The method processes voxelized occupancy grids instead of memory-intensive TSDFs, achieving an impressive IoU of 55.32 on the SemanticKITTI benchmark.
• The multiscale mixed 2D/3D convolution design enables faster, smoother scene reconstruction, making it suitable for real-time applications in robotics and autonomous driving.

Overview of LMSCNet: Lightweight Multiscale 3D Semantic Completion

The paper "LMSCNet: Lightweight Multiscale 3D Semantic Completion," authored by Roldão et al., proposes a novel approach for tackling the challenge of 3D semantic scene completion from voxelized sparse 3D LiDAR scans. The methodology and implementation of the proposed LMSCNet highlight significant strides in both performance and efficiency compared to extant techniques.

Technical Contributions

LMSCNet is structured around notable departures from conventional methods:

1. 2D UNet Backbone with Multiscale Skip Connections: Unlike traditional voxel grid processing methodologies that often employ 3D convolutions, LMSCNet utilizes a 2D UNet architecture. This approach optimizes feature flow by integrating comprehensive multiscale skip connections and interfaces with 3D segmentation heads.
2. Occupancy Grid Processing: By avoiding memory-intensive representations like TSDFs, LMSCNet processes inputs as 3D voxelized occupancy data, thereby aligning more closely with realistic scene completion tasks which are often limited by sensor field-of-view and inherent sparsity.
3. Multiscale Architecture: The design incorporates a lightweight architecture with mixed 2D/3D convolutions, enabling multiscale semantic completion. This multi-resolution capacity allows for flexible parsing of the scene at various scales, providing potential efficiency gains in resolution-variable applications such as autonomous driving.

Performance and Results

The proposed LMSCNet achieves a competitive balance between computational efficiency and performance accuracy. On the challenging SemanticKITTI benchmark, LMSCNet demonstrates state-of-the-art performance in semantic completion tasks, surpassing existing approaches in occupancy completion while maintaining a significantly lighter computational burden which makes it apt for mobile robotics applications.

Numerically, LMSCNet attains an IoU score of 55.32 on scene completion, outperforming similarly classified models. It also achieves high recall values at reduced computational times, validating its design efficiency for embedded systems in robotics and virtual reality domains. The network architecture achieves semantic scenes with smoother labels and is capable of discerning fine structures, as evident in enhanced scene reconstructions observed during qualitative evaluations.

Implications and Future Prospects

LMSCNet’s methodical blend of 2D and 3D convolutions with a multiscale capability sets a precedent for future research targeting lightweight, deployable models in real-time applications. The multiscale mechanism is particularly noteworthy, as it offers speed-performance trade-offs crucial for time-sensitive tasks in autonomous systems. Additionally, robustness to input density variations extends its utility across different sensor configurations, underlining its versatility.

Future developments could refine this framework by enhancing multiscale feature integration and extending adaptability across more diverse environments and datasets, potentially further reducing computation overheads. Moreover, coupling LMSCNet with advanced real-time LiDAR sensors could enhance models for intricate and dynamic unrestored urban environments.

In conclusion, the LMSCNet architecture presents an innovative balance between efficiency and performance precision, marking an important progression in 3D semantic scene completion methodologies. Its robust performance in real-world applications aligns with advancements towards deploying computational models on minimalistic hardware, paving the path for broader adoption in autonomous technologies.