[2603.00651] Exploring 3D Dataset Pruning

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00651 (cs) [Submitted on 28 Feb 2026] Title:Exploring 3D Dataset Pruning Authors:Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Zhiqiang Shen View a PDF of the paper titled Exploring 3D Dataset Pruning, by Xiaohan Zhao and Xinyi Shang and Jiacheng Liu and Zhiqiang Shen View PDF HTML (experimental) Abstract:Dataset pruning has been widely studied for 2D images to remove redundancy and accelerate training, while particular pruning methods for 3D data remain largely unexplored. In this work, we study dataset pruning for 3D data, where its observed common long-tail class distribution nature make optimization under conventional evaluation metrics Overall Accuracy (OA) and Mean Accuracy (mAcc) inherently conflicting, and further make pruning particularly challenging. To address this, we formulate pruning as approximating the full-data expected risk with a weighted subset, which reveals two key errors: coverage error from insufficient representativeness and prior-mismatch bias from inconsistency between subset-induced class weights and target metrics. We propose representation-aware subset selection with per-class retention quotas for long-tail coverage, and prior-invariant teacher supervision using calibrated soft labels and embedding-geometry distillation. The retention quota also serves as a switch to control the OA-mAcc trade-off. Extensive experiments on 3D datasets show that our method can improve both metrics ...