Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data (2301.00527v1)

Abstract: In this paper, we learn a diffusion model to generate 3D data on a scene-scale. Specifically, our model crafts a 3D scene consisting of multiple objects, while recent diffusion research has focused on a single object. To realize our goal, we represent a scene with discrete class labels, i.e., categorical distribution, to assign multiple objects into semantic categories. Thus, we extend discrete diffusion models to learn scene-scale categorical distributions. In addition, we validate that a latent diffusion model can reduce computation costs for training and deploying. To the best of our knowledge, our work is the first to apply discrete and latent diffusion for 3D categorical data on a scene-scale. We further propose to perform semantic scene completion (SSC) by learning a conditional distribution using our diffusion model, where the condition is a partial observation in a sparse point cloud. In experiments, we empirically show that our diffusion models not only generate reasonable scenes, but also perform the scene completion task better than a discriminative model. Our code and models are available at https://github.com/zoomin-lee/scene-scale-diffusion

Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data

The paper "Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data" by Lee et al. presents a novel approach to generating 3D data at a scene scale using diffusion probabilistic models. This paper extends recent research that has predominantly focused on single-object generation to encompass entire scenes containing multiple objects. This innovative leap is achieved through a nuanced implementation of discrete diffusion models adapted for 3D categorical distributions, providing a robust framework for generating complex 3D scenes.

Diffusion models have gained traction for their proficiency in generating high-dimensional data. Prior applications have largely been confined to continuous data and lower-dimensional tasks. This paper, however, ambitiously ventures into discrete state spaces, modeling voxel-based 3D segmentation maps. Scene-scale generation demands methods that can intuitively offer spatial coherence in object placement and category assignments, challenges the proposed model effectively addresses. By leveraging categorical data for voxel entities, the paper aligns semantic categorization with diffusion processes, breaking new ground in 3D scene modeling.

A notable advancement within this paper is the extension of discrete diffusion models, originally established for 2D data, to 3D categorical data. This involves training models to navigate the complex task of generating volumetric semantic scenes and performing semantic scene completion (SSC). SSC involves filling in occluded and sparse areas based on partial observations, such as those acquired from LiDAR scans. The proposed diffusion model demonstrates a significantly improved capacity to handle such tasks compared to traditional discriminative models, showcasing both unconditional and conditional generation capabilities.

The implementation of latent diffusion models in this context is particularly compelling. By shifting computation from the voxel space to a compressed latent space, the model achieves reduced computational costs and expedited inference times. This compression is facilitated by a Vector-Quantized Variational Auto-Encoder (VQ-VAE), which efficiently encodes high-resolution segmentation maps into lower-dimensional latent spaces without sacrificing essential spatial and semantic information. The efficiency gains observed in both training and inference highlight the practical benefits of this approach, presenting latent diffusion as a viable alternative to more computationally intensive methodologies.

In terms of empirical validation, the diffusion models demonstrate superior performance in generating realistic 3D scenes and completing semantically coherent scene reconstructions. Through an array of experiments on the CarlaSC dataset, the models show marked improvements over existing state-of-the-art methods, particularly in scenarios involving complex object interactions within a scene. These results underscore the potential for diffusion models to revolutionize 3D scene generation tasks, pushing the envelope in areas such as autonomous vehicle simulation and virtual reality environments.

Looking forward, the application of diffusion models in scene-scale 3D data generation presents promising avenues for further research. Potential directions include optimizing the architecture for even greater computational efficiency or enhancing the conditional generation capabilities to encompass more challenging and dynamic 3D environments. Moreover, integrating real-time capabilities into this framework could open up new possibilities in industries where rapid scene generation is critical.

In conclusion, this paper successfully extends diffusion probabilistic models into the scene-scale 3D categorical domain, offering a significant contribution to the field of 3D data generation. Through a combination of theoretical innovation and practical efficiency, the proposed models provide a strong foundation for future research and development in complex 3D scene generation and understanding.