We show that this continuous representation is suitable to encode geometric and semantic properties of extensive outdoor scenes without the need for spatial discretization (thus avoiding the trade-off between level of scene detail and the scene extent that can be covered).
Ranked #4 on 3D Semantic Scene Completion on SemanticKITTI
In experiments on a real-life dataset we demonstrate that our method outperforms the state-of-the-art methods both target- and object-wise by reaching an average of 0. 70 (baseline: 0. 68) target-wise and 0. 56 (baseline: 0. 48) object-wise F1 score.
With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important.
The current paradigm in privacy protection in street-view images is to detect and blur sensitive information.