DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models (2409.18092v2)

Abstract: Perception systems play a crucial role in autonomous driving, incorporating multiple sensors and corresponding computer vision algorithms. 3D LiDAR sensors are widely used to capture sparse point clouds of the vehicle's surroundings. However, such systems struggle to perceive occluded areas and gaps in the scene due to the sparsity of these point clouds and their lack of semantics. To address these challenges, Semantic Scene Completion (SSC) jointly predicts unobserved geometry and semantics in the scene given raw LiDAR measurements, aiming for a more complete scene representation. Building on promising results of diffusion models in image generation and super-resolution tasks, we propose their extension to SSC by implementing the noising and denoising diffusion processes in the point and semantic spaces individually. To control the generation, we employ semantic LiDAR point clouds as conditional input and design local and global regularization losses to stabilize the denoising process. We evaluate our approach on autonomous driving datasets and our approach outperforms the state-of-the-art for SSC.

• The paper leverages DDPMs to accurately reconstruct dense 3D semantic scenes from sparse LiDAR data by modeling point and semantic spaces separately.
• The method outperforms existing techniques on datasets like SemanticKITTI and SSCBench-KITTI360, achieving significant improvements in IoU metrics.
• Its innovative diffusion-based framework enhances real-time perception for autonomous vehicles by efficiently filling occluded or missing environment details.

DiffSSC: Semantic LiDAR Scan Completion Using Denoising Diffusion Probabilistic Models

The paper "DiffSSC: Semantic LiDAR Scan Completion using Denoising Diffusion Probabilistic Models" introduces an innovative approach to Semantic Scene Completion (SSC) for autonomous driving applications. The challenge addressed by this work arises from the inherent limitations of 3D LiDAR sensors, which produce sparse point clouds that impede the perception systems' ability to perceive occluded areas and fill gaps due to the absence of semantic data. This problem is pivotal for autonomous vehicles, where a comprehensive understanding of the environment is crucial for safe navigation.

The authors propose leveraging Denoising Diffusion Probabilistic Models (DDPMs) to predict unobserved geometry and semantics from partial LiDAR data, filling a significant gap in autonomous driving perception systems. This methodology incorporates state-of-the-art machine learning techniques to achieve superior scene completion results compared to existing methods. By employing DDPMs, the authors extend the applications of diffusion models beyond image synthesis and super-resolution, effectively applying them in the three-dimensional space.

Methodological Considerations

The authors' approach is distinctly characterized by modeling both point and semantic spaces separately, enhancing the adaptability to a diffusion process. The diffusion model iteratively introduces noise into the data and leverages a denoising step to reconstruct the original scene. By controlling this process using semantic LiDAR point clouds as conditional inputs, the authors ensure the generation process aligns with realistic scene expectations.

Key technical contributions include:

• Utilization of DDPMs: By applying DDPMs for SSC, the authors introduce a novel residual-learning mechanism compared to traditional approaches like directly estimating scenes from incomplete inputs.
• Separation of Point and Semantic Spaces: This methodological choice allows the model to be more flexible and efficient, optimizing both the diffusion and denoising processes.
• Efficiency in Processing LiDAR Data: Unlike voxel-based methods that often suffer from quantization errors and increased memory usage, the presented approach operates directly on point clouds, maintaining higher resolution and reduced computation demands.
• Regularization Strategies: The authors design local and global regularization losses to stabilize the learning process, enhancing the convergence and reliability of scene estimation.

Numerical Performance and Implications

The paper presents robust empirical results, showcasing DiffSSC's ability to surpass state-of-the-art methods in autonomous driving datasets like SemanticKITTI and SSCBench-KITTI360. Notably, the method yields significant improvements in IoU metrics, underscoring its effectiveness in dense and semantic scene estimation.

The implications of this work are profound both practically and theoretically:

• Practical Applications: Enhanced scene completion will improve perception systems in autonomous vehicles, particularly in unusual or occluded scenarios where decision-making is critical.
• Theoretical Advancements: Demonstrates the adaptability and efficacy of diffusion models beyond traditional 2D domains, paving the path for future research on DDPMs in diverse applications including 3D modeling and robotics.

Future Directions in AI

The authors suggest several promising avenues for future research. These include optimizing the inference process to enhance real-time applicability and exploring adaptive noise schedules to improve the generative quality within DDPMs. By expanding research in these areas, the utility and performance of diffusion models can continue to evolve, further bolstering their integration into complex autonomous systems.

In summary, this paper proposes a sophisticated framework for semantic scene completion using diffusion models, marking a significant step forward in the efficient processing and interpretation of sparse LiDAR point clouds. The incorporation of DDPMs into SSC offers meaningful contributions to both the machine learning community and the field of autonomous driving.