  Prediction beyond partial observations is crucial for robots to navigate in
unknown environments because it can provide extra information regarding the
surroundings beyond the current sensing range or resolution. In this work, we
consider the inpainting of semantic Bird's-Eye-View maps. We propose SePaint,
an inpainting model for semantic data based on generative multinomial
diffusion. To maintain semantic consistency, we need to condition the
prediction for the missing regions on the known regions. We propose a novel and
efficient condition strategy, Look-Back Condition (LB-Con), which performs
one-step look-back operations during the reverse diffusion process. By doing
so, we are able to strengthen the harmonization between unknown and known
parts, leading to better completion performance. We have conducted extensive
experiments on different datasets, showing our proposed model outperforms
commonly used interpolation methods in various robotic applications.
