arXiv subjects

Shinichi Mae

Publications and source records attributed to Shinichi Mae.

2 recordsLinked to original sources

arXiv2025

Industrial Synthetic Segment Pre-training

Vision Foundation Models (VFMs) have made remarkable progress and are increasingly being applied to segmentation tasks in real-world industrial settings. However, VFMs pre-trained on real-image datasets still face several challenges: (1) they do not always perform well on industrial datasets due to significant differences from natural imagery, (2) legal and ethical restrictions, such as limitations on commercial use, constrain extensibility, and (3) building training frameworks under limited computational and data resources remains a critical issue. These challenges raise a fundamental question: can we construct industrial segmentation models without relying on real images or manual annotations? To address this question, we propose the Instance Core Segment Dataset (InsCore), a synthetic data generation framework and the resulting pre-training dataset based on Formula-Driven Supervised Learning (FDSL). InsCore is designed not around the visual appearance or domain of real images, but around the hypothesis that learning to handle complex occlusions during pre-training is a key factor for strong performance in industrial domains. Through experiments across five domains (medical, biomedical, remote sensing, manufacturing, and logistics) we demonstrate that InsCore pre-trained models achieve average mAP scores of 45.2 with the ViTDet backbone and 46.0 with the Swin Transformer backbone, on par with ImageNet-21k supervised pre-training (45.0) while using no real images at all. As a reference point under different input assumptions, prompted SAM with ground-truth bounding boxes attains 45.4 on the same benchmarks. Finally, InsCore consists of only 100k images and 3.2M masks, roughly 1/110 and 1/312 the scale of the SA-1B dataset.

cs.CV↗

arXiv2025

Pre-training with 3D Synthetic Data: Learning 3D Point Cloud Instance Segmentation from 3D Synthetic Scenes

In the recent years, the research community has witnessed growing use of 3D point cloud data for the high applicability in various real-world applications. By means of 3D point cloud, this modality enables to consider the actual size and spatial understanding. The applied fields include mechanical control of robots, vehicles, or other real-world systems. Along this line, we would like to improve 3D point cloud instance segmentation which has emerged as a particularly promising approach for these applications. However, the creation of 3D point cloud datasets entails enormous costs compared to 2D image datasets. To train a model of 3D point cloud instance segmentation, it is necessary not only to assign categories but also to provide detailed annotations for each point in the large-scale 3D space. Meanwhile, the increase of recent proposals for generative models in 3D domain has spurred proposals for using a generative model to create 3D point cloud data. In this work, we propose a pre-training with 3D synthetic data to train a 3D point cloud instance segmentation model based on generative model for 3D scenes represented by point cloud data. We directly generate 3D point cloud data with Point-E for inserting a generated data into a 3D scene. More recently in 2025, although there are other accurate 3D generation models, even using the Point-E as an early 3D generative model can effectively support the pre-training with 3D synthetic data. In the experimental section, we compare our pre-training method with baseline methods indicated improved performance, demonstrating the efficacy of 3D generative models for 3D point cloud instance segmentation.

cs.CV↗