RGBD20K: A Large-Scale Benchmark for RGB-D Semantic Segmentation
RGBD20K adds 20,000 RGB-D pairs and 160 classes for semantic segmentation, plus a score-purified fusion method.
RGBD20K is a semantic-segmentation benchmark with 20,000 RGB-D image pairs and 160 fine-grained categories, far more classes than NYUv2 (40) or SUN RGB-D (37). The authors re-check and correct existing labels to reduce annotation noise. They also introduce score-purified fusion (SPF), which they report as state of the art across the benchmarks they evaluate. The dataset is released on GitHub.
- Covers 160 fine-grained classes, versus 40 in NYUv2 and 37 in SUN RGB-D.
- Offers 20,000 RGB-D pairs, a larger training resource than popular benchmarks.
- Labels were re-evaluated and corrected to reduce long-standing annotation noise.
- Score-purified fusion reports state-of-the-art results on the evaluated benchmarks.
From the original article
1 min read at huggingface.co
In this paper, we propose RGBD20K, a novel dataset for facilitating the development of more robust and general RGB-D semantic segmentation by encompassing abundant categories and high-quality annotations. RGBD20K possesses several attractive properties: (1) Expanded Semantic Space. In particular, it covers 160 fine-grained categories, largely surpassing the category diversity of existing popular RGB-D benchmarks (e.
By Shaohua Dong, Zexuan Meng, Haiyan Sun · huggingface.co
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