IBims-1 (Independent benchmark images and matched scans v1)

Introduced by Koch et al. in Evaluation of CNN-based Single-Image Depth Estimation Methods

iBims-1 (independent Benchmark images and matched scans - version 1) is a new high-quality RGB-D dataset, especially designed for testing single-image depth estimation (SIDE) methods. A customized acquisition setup, composed of a digital single-lens reflex (DSLR) camera and a high-precision laser scanner was used to acquire high-resolution images and highly accurate depth maps of diverse indoors scenarios.

Compared to related RGB-D datasets, iBims-1 stands out due to a very low noise level, sharp depth transitions, no occlusions, and high depth ranges.

Our dataset consists of the following components:

Core dataset:

  • 100 RGB-D image pairs of various indoor scenes in high- and low resolution
  • Masks for invalid, transparent and planar regions (tables, floors, walls)
  • Masks for distinct depth transitions
  • Camera calibration parameters

Auxiliary dataset: - 56 different color and geometric augmentations for each image of the core dataset - Additional hand-held images for testing MVS methods - Images of printed patterns and photos posted on a wall to assess performance of textured planar surfaces - Several RGB-D image sequences of static scenes with varying illumation

Source: Evaluation of CNN-based Single-Image Depth Estimation Methods


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