# A Parametric Top-View Representation of Complex Road Scenes

## TL;DR

This paper introduces a parametric top-view model for complex road scenes, combined with a neural network trained on simulated and real data, and a CRF for temporal smoothing, improving scene understanding from a single camera.

## Contribution

The paper presents a novel parametric top-view scene model, a training method using domain adaptation with simulated and real data, and a CRF for temporal consistency in road scene inference.

## Key findings

- The top-view model effectively describes complex road scenes.
- The combined training approach outperforms models trained on only simulated or real data.
- The CRF produces temporally smooth and semantically coherent predictions.

## Abstract

In this paper, we address the problem of inferring the layout of complex road scenes given a single camera as input. To achieve that, we first propose a novel parameterized model of road layouts in a top-view representation, which is not only intuitive for human visualization but also provides an interpretable interface for higher-level decision making. Moreover, the design of our top-view scene model allows for efficient sampling and thus generation of large-scale simulated data, which we leverage to train a deep neural network to infer our scene model's parameters. Specifically, our proposed training procedure uses supervised domain-adaptation techniques to incorporate both simulated as well as manually annotated data. Finally, we design a Conditional Random Field (CRF) that enforces coherent predictions for a single frame and encourages temporal smoothness among video frames. Experiments on two public data sets show that: (1) Our parametric top-view model is representative enough to describe complex road scenes; (2) The proposed method outperforms baselines trained on manually-annotated or simulated data only, thus getting the best of both; (3) Our CRF is able to generate temporally smoothed while semantically meaningful results.