Group-wise Supervision with Focal-Dice Loss for Long-Tailed Indoor Semantic Occupancy Prediction
Quick Answer
The proposed Group-UFD Occ method enhances long-tailed indoor semantic occupancy prediction by 11.38% over baseline using hierarchical supervision and Unified Focal-Dice loss, effectively addressing the challenges posed by diverse object categories.
Quick Take
This approach employs fine-grained semantic grouping and multi-scale prediction heads to improve tail-class feature learning.
Key Points
- Introduces Group-UFD Occ for improved indoor semantic occupancy prediction.
- Achieves 11.38% relative improvement on the EmbodiedScan dataset.
- Utilizes hierarchical semantic supervision and synergistic loss optimization.
- Employs fine-grained semantic grouping and multi-scale prediction heads.
- Focuses on hard samples with Unified Focal-Dice loss at the voxel level.
Paper Resources
Source Excerpt
Recently, 3D semantic occupancy prediction has garnered increasing attention for understanding the indoor scene. However, unlike structured outdoor environments, indoor scenes feature a high diversity of object categories that exhibit a severe long-tailed distribution, which has become a core bottleneck limiting the performance of existing models. To tackle this challenge, we propose a novel method, Group-UFD Occ, based on hierarchical semantic supervision and synergistic loss optimization. At t
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