NeuralFloors++: Consistent Street-Level Scene Generation From BEV Semantic Maps

Learning autonomous driving capabilities requires diverse and realistic training data. This has led to exploring generative techniques as an alternative to real-world data collection. In this paper we propose a method for synthesising photo-realistic urban driving scenes, along with semantic, instance and depth ground-truth. Our model relies on Bird’s Eye View (BEV) representations due to their compositionality and scene content control capabilities, reducing the need for traditional simulators. We employ a two-stage process: first, a 3D scene representation is extracted from BEV semantic, instance and style maps using a neural field. After rendering the semantic, instance, depth and style maps from a ground-view perspective, a second stage based on a diffusion model is used to generate the photo-realistic scene. We extend our prior work - NeuralFloors, to include multiple-view outputs, style manipulation for finer control at the object level through instance-wise style maps and cross-frame consistency via auto-regressive training. The proposed system is evaluated extensively on the KITTI-360 dataset, showing improved realism and semantic alignment for generated images.