Booster Dataset

We present a novel high-resolution and challenging stereo dataset framing indoor scenes annotated with dense and accurate ground-truth disparities. Peculiar to our dataset is the presence of several specular and transparent surfaces, i.e. the main causes of failures for state-of-the-art stereo networks. Our acquisition pipeline leverages a novel deep space-time stereo framework which allows for easy and accurate labeling with sub-pixel precision. We release a total of 419 samples collected in 64 different scenes and annotated with dense ground-truth disparities. Each sample include a high-resolution pair (12 Mpx) as well as an unbalanced pair (Left: 12 Mpx, Right: 1.1 Mpx). Additionally, we provide manually annotated material segmentation masks and 15K unlabeled samples. We evaluate state-of-the-art deep networks based on our dataset, highlighting their limitations in addressing the open challenges in stereo and drawing hints for future research.本研究提出一款新颖的高分辨率、高挑战性的室内场景立体视觉数据集，其标注有稠密且精确的真值视差（ground-truth disparities）。该数据集的独特之处在于包含多种镜面反射与透明表面——而这类表面正是当前顶尖立体视觉网络失效的核心诱因。我们的采集流程采用了新颖的深度时空立体框架，可实现亚像素级精度的便捷精准标注。本次共发布64个不同场景下采集的419组样本，所有样本均标注有稠密的真值视差。每组样本均包含一组高分辨率图像对（12 Mpx），以及一组非均衡图像对（左图：12 Mpx，右图：1.1 Mpx）。此外，我们还提供了人工标注的材质分割掩码与15000组未标注样本。我们基于该数据集对当前顶尖的深度神经网络开展评测，揭示了现有方法在应对立体视觉领域未决挑战时的局限性，并为后续研究提供了参考方向。

搜集汇总

背景与挑战

背景概述

Booster Dataset是一个高分辨率室内立体数据集，包含419个样本，覆盖64个场景，提供12 Mpx高分辨率立体对和不平衡立体对，并标注密集地面真实视差。其特点在于包含镜面和透明表面，这些是现有立体网络的主要挑战点，同时提供材料分割掩模和15K未标注样本，旨在推动立体视觉研究。