  Existing solutions for 3D semantic occupancy prediction typically treat the
task as a one-shot 3D voxel-wise segmentation perception problem. These
discriminative methods focus on learning the mapping between the inputs and
occupancy map in a single step, lacking the ability to gradually refine the
occupancy map and the reasonable scene imaginative capacity to complete the
local regions somewhere. In this paper, we introduce OccGen, a simple yet
powerful generative perception model for the task of 3D semantic occupancy
prediction. OccGen adopts a ''noise-to-occupancy'' generative paradigm,
progressively inferring and refining the occupancy map by predicting and
eliminating noise originating from a random Gaussian distribution. OccGen
consists of two main components: a conditional encoder that is capable of
processing multi-modal inputs, and a progressive refinement decoder that
applies diffusion denoising using the multi-modal features as conditions. A key
insight of this generative pipeline is that the diffusion denoising process is
naturally able to model the coarse-to-fine refinement of the dense 3D occupancy
map, therefore producing more detailed predictions. Extensive experiments on
several occupancy benchmarks demonstrate the effectiveness of the proposed
method compared to the state-of-the-art methods. For instance, OccGen
relatively enhances the mIoU by 9.5%, 6.3%, and 13.3% on nuScenes-Occupancy
dataset under the muli-modal, LiDAR-only, and camera-only settings,
respectively. Moreover, as a generative perception model, OccGen exhibits
desirable properties that discriminative models cannot achieve, such as
providing uncertainty estimates alongside its multiple-step predictions.
