Panoptic-Depth Forecasting

• URL: http://arxiv.org/abs/2409.12008v1
• Date: Wed, 18 Sep 2024 14:21:07 GMT
• Title: Panoptic-Depth Forecasting
• Authors: Juana Valeria Hurtado, Riya Mohan, Abhinav Valada,
• Abstract summary: We propose the panoptic-depth forecasting task for jointly predicting the panoptic segmentation and depth maps of unobserved future frames. We extend the popular KITTI-360 and Cityscapes benchmarks by computing depth maps from LiDAR point clouds and leveraging labeled data. We present two baselines and propose the novel PDcast architecture that learns rich-temporal representations by incorporating a transformer-based encoder, a forecasting module, and task-specific decoders.
• Score: 8.81078960241057
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting the semantics and 3D structure of scenes is essential for robots to navigate and plan actions safely. Recent methods have explored semantic and panoptic scene forecasting; however, they do not consider the geometry of the scene. In this work, we propose the panoptic-depth forecasting task for jointly predicting the panoptic segmentation and depth maps of unobserved future frames, from monocular camera images. To facilitate this work, we extend the popular KITTI-360 and Cityscapes benchmarks by computing depth maps from LiDAR point clouds and leveraging sequential labeled data. We also introduce a suitable evaluation metric that quantifies both the panoptic quality and depth estimation accuracy of forecasts in a coherent manner. Furthermore, we present two baselines and propose the novel PDcast architecture that learns rich spatio-temporal representations by incorporating a transformer-based encoder, a forecasting module, and task-specific decoders to predict future panoptic-depth outputs. Extensive evaluations demonstrate the effectiveness of PDcast across two datasets and three forecasting tasks, consistently addressing the primary challenges. We make the code publicly available at https://pdcast.cs.uni-freiburg.de.

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