300万对样本、200万对实拍：深度估计的数据荒，终于被打破

Core Viewpoint - The article discusses the limitations of existing depth estimation and completion models due to reliance on outdated datasets, highlighting the significance of the newly released LingBot-Depth-Dataset by Ant Group, which provides a large-scale, high-quality RGB-depth dataset to enhance model training and performance in real-world applications [4][5][34]. Group 1: Dataset Overview - Ant Group has open-sourced approximately 3 million pairs of high-quality RGB-depth data, making it one of the largest real-world RGB-D datasets available [5][16]. - The dataset consists of 2.71TB of data, including around 2 million pairs of real RGB-D data and 1 million pairs of high-quality rendered data, covering six mainstream depth cameras [5][6]. - The dataset is structured into four subsets: RobbyReal, RobbyVla, RobbySim, and RobbySimVal, each designed to address specific challenges in depth perception tasks [17][22][24]. Group 2: Importance of Real Data - The article emphasizes the challenges in obtaining high-quality real RGB-D data, including high costs, technical complexities, and the inherent limitations of depth sensors [12][13][14]. - The lack of large-scale real-world RGB-D datasets has created a gap in the field, which the LingBot-Depth-Dataset aims to fill, providing a critical resource for advancing depth estimation technologies [14][34]. - The dataset's design allows models to learn from diverse sensor characteristics, improving their generalization across different hardware environments [19][20]. Group 3: Impact on the Industry - The introduction of the LingBot-Depth-Dataset is expected to shift the focus from model complexity to data quality, as the performance of models is increasingly determined by the quality and quantity of training data [31][32]. - This dataset could serve as a new benchmark for depth estimation and completion, similar to how ImageNet transformed visual recognition [34][35]. - By providing a comprehensive dataset, Ant Group enables research teams to concentrate on higher-level problems without the need to collect data from scratch, fostering innovation in the field [36].