Bird Eye View Representations or simply “bird’s eye view” refers to a perspective that simulates the view from above, as if seen from a high-flying bird. In the context of mapping or visualization, this perspective provides a comprehensive and detailed view of a scene or environment from an elevated position.


A Dataset for High-Level 3D Scene Understanding of Complex Road Scenes in the Top-View

We introduce a novel dataset for high-level 3D scene understanding of complex road scenes. Our annotations extend the existing datasets KITTI [5] and nuScenes [1] with semantically and geometrically meaningful attributes like the number of lanes or the existence of, and distance to, intersections, sidewalks and crosswalks. Our attributes are rich enough to build a meaningful representation of the scene in the top-view and provide a tangible interface to the real world for several practical applications.

A Parametric Top-View Representation of Complex Road Scenes

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.