5 accepted papers
Simulation with realistic and interactive agents represents a key task for autonomous vehicle (AV) software development in order to test AV performance in prescribed, often long-tail scenarios. In this work, we propose SceneDiffuser, a scene-level diffusion prior for traffic simulation. We present a…
Widely adopted motion forecasting datasets sub-stitute the observed sensory inputs with higher-level abstractions such as 3D boxes and polylines. These sparse shapes are inferred through annotating the original scenes with perception systems’ predictions. Such intermediate representations tie the qu…
Lidars are depth measuring sensors widely used in autonomous driving and augmented reality. However, the large volume of data produced by lidars can lead to high costs in data storage and transmission. While lidar data can be represented as two interchangeable representations: 3D point clouds and ra…
It is challenging to annotate large-scale datasets for supervised video shadow detection methods. Using a model trained on labeled images to the video frames directly may lead to high generalization error and temporal inconsistent results. In this paper, we address these challenges by proposing a Sp…
Earlier work demonstrates the promise of deep-learning-based approaches for point cloud segmentation; however, these approaches need to be improved to be practically useful. To this end, we introduce a new model SqueezeSegV2. With an improved model structure, SqueezeSetV2 is more robust against drop…