Discrete Diffusion-Based Generative Semantic Scene Completion

Semantic scene completion through AI-driven content generation is a rapidly evolving field with crucial applications in 3D reconstruction and scene understanding. This task presents considerable challenges, arising from the intrinsic data sparsity and incomplete nature of input points generated by LiDAR. This paper proposes a generative semantic scene completion method based on a discrete denoising diffusion probabilistic model to tackle these issues. In the discrete diffusion phase, a weighted K-nearest neighbor uniform transition kernel is introduced based on feature distance in the discretized voxel space to control the category distribution transition processes by capturing the local structure of data, which is more in line with the diffusion process in the real world. Moreover, to mitigate the feature information loss during point cloud voxelization, the aggregated point features are integrated into the corresponding voxel space, thereby enhancing the granularity of the completion. Accordingly, a combined loss function is designed for network training that considers both the KL divergence for global completion and the cross-entropy for local details. The evaluation, which results from multiple public outdoor datasets, demonstrates that the proposed method effectively accomplishes semantic scene completion.