Papers › Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Efficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Daniel Seichter, Söhnke Benedikt Fischedick, Mona Köhler, Horst-Michael Groß
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general scene are missing but required for many real-world applications. However, solving multiple tasks separately is expensive and cannot be accomplished in real time given limited computing and battery capabilities on a mobile platform. In this paper, we propose an efficient multi-task approach for RGB-D scene analysis~(EMSANet) that simultaneously performs semantic and instance segmentation~(panoptic segmentation), instance orientation estimation, and scene classification. We show that all tasks can be accomplished using a single neural network in real time on a mobile platform without diminishing performance - by contrast, the individual tasks are able to benefit from each other. In order to evaluate our multi-task approach, we extend the annotations of the common RGB-D indoor datasets NYUv2 and SUNRGB-D for instance segmentation and orientation estimation. To the best of our knowledge, we are the first to provide results in such a comprehensive multi-task setting for indoor scene analysis on NYUv2 and SUNRGB-D.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Panoptic Segmentation | NYU Depth v2 | EMSANet | PQ | 47.38 | #2 of 2 | Archive leaderboard | report |
| Panoptic Segmentation | SUN-RGBD | EMSANet | PQ | 52.84 | #1 of 1 | Archive leaderboard | report |
| Semantic Segmentation | NYU Depth v2 | EMSANet (2x ResNet-34 NBt1D, finetuned) | Mean IoU | 53.34% | #37 of 121 | Archive leaderboard | report |
| Semantic Segmentation | SUN-RGBD | DPLNet | Mean IoU | 48.47% | #30 of 44 | Archive leaderboard | report |
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