This paper presents Objaverse++, a curated subset of Objaverse enhanced with detailed attribute annotations. Recent advances in 3D content generation have been driven by large-scale datasets such as Objaverse [6], which contains over 800,000 3D objects collected from the Internet. To address the issue of the prevalence of low-quality models in Objaverse, human experts manually annotate 10,000 3D objects with detailed attributes, including aesthetic quality scores, texture color classifications, multi-object composition flags, transparency characteristics, etc. Then, we trained a neural network capable of annotating the tags for the rest of the Objaverse dataset. Through experiments and a user study on generation results, we demonstrate that models pre-trained on our quality-focused subset achieve better performance than those trained on the larger dataset of Objaverse in image-to-3D generation tasks. In addition, by comparing multiple subsets of training data filtered by our tags, our results show that the higher the data quality, the faster the training loss converges. These findings suggest that careful curation and rich annotation can compensate for the raw dataset size, potentially offering a more efficient path to develop 3D generative models. We released our enhanced dataset of approximately 790,000 curated 3D models to facilitate further research on various downstream tasks in 3D computer vision and aim to extend our annotations to cover the entire Objaverse dataset.