SemSegDepth: A Combined Model for Semantic Segmentation and Depth Completion

2022-09-01 · Juan Pablo Lagos, Esa Rahtu

Holistic scene understanding is pivotal for the performance of autonomous machines. In this paper we propose a new end-to-end model for performing semantic segmentation and depth completion jointly. The vast majority of recent approaches have developed semantic segmentation and depth completion as independent tasks. Our approach relies on RGB and sparse depth as inputs to our model and produces a dense depth map and the corresponding semantic segmentation image. It consists of a feature extractor, a depth completion branch, a semantic segmentation branch and a joint branch which further processes semantic and depth information altogether. The experiments done on Virtual KITTI 2 dataset, demonstrate and provide further evidence, that combining both tasks, semantic segmentation and depth completion, in a multi-task network can effectively improve the performance of each task. Code is available at https://github.com/juanb09111/semantic depth.

📄 PDF Abstract BibTeX arXiv:2209.00381

Code (1)

juanb09111/semantic_depth 공식 구현 pytorch

Tasks

Depth Completion Scene Understanding Segmentation Semantic Segmentation

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Semantic Segmentation Instance Segmentation Autonomous Driving Depth Completion

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Understanding 3D environments semantically is pivotal in autonomous driving applications where multiple computer vision tasks are involved. Multi-task models provide different types of outputs for a given scene, yielding…

Autonomous Driving Depth Completion Instance Segmentation Panoptic Segmentation+2