Technology Category
Computer Vision: 3D Computer VisionNatural Language Processing: Code Generation / Program Synthesis from Natural LanguageMachine Learning: Deep Generative Models & Autoencoders
Pre-training with 3D Synthetic Data: Learning 3D Point Cloud Instance Segmentation from 3D Synthetic Scenes
🤖 AI Summary
To address the high annotation cost of real 3D point cloud data—which severely limits model scalability and generalization in point cloud instance segmentation—this paper proposes a novel pretraining paradigm leveraging synthetic 3D data. We are the first to directly utilize the text-to-3D generative model Point-E to synthesize large-scale, photorealistic point cloud scenes with precise, instance-level annotations. Building upon this, we introduce a two-stage pretraining–fine-tuning framework tailored for downstream instance segmentation tasks (e.g., PointGroup). Our approach substantially reduces reliance on costly real-world annotations while significantly improving segmentation accuracy and cross-scene generalization on benchmarks such as ScanNet—outperforming both non-pretrained and image-level pretrained baselines. The core contribution lies in establishing a 3D generative model–driven, instance-aware synthetic data pretraining pipeline, offering a new paradigm for low-annotation-cost, robust 3D scene understanding.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
High cost of creating 3D point cloud datasets for instance segmentation.
Need for detailed annotations in large-scale 3D space for training.
Proposing pre-training with synthetic 3D data to improve segmentation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Pre-training with 3D synthetic data
Using Point-E for 3D point cloud generation
Improving 3D instance segmentation performance
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