iBims-1 (independent Benchmark images and matched scans - version 1) is a high-quality RGB-D dataset created for evaluation of single-image (monocular) depth estimation methods. It was captured with a DSLR camera together with a high-precision laser scanner to provide high-resolution RGB images and highly accurate depth maps with low noise, sharp depth transitions, minimal occlusions and a large depth range. The dataset was designed to support geometry-aware evaluation metrics (e.g., edge/planarity preservation, absolute distance accuracy) and includes per-image masks for invalid/transparent regions and for planar or sharp depth-transition areas, as well as camera calibration parameters. The core release contains 100 RGB–depth image pairs from indoor scenes; the authors also provide an extension with additional variations (reported as 56 variants/extensions and several additional sequences and test images). The dataset and its evaluation protocol were introduced alongside the paper “Evaluation of CNN-based Single-Image Depth Estimation Methods” (ECCV Workshops 2018 / arXiv:1805.01328).
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