MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data

Abstract: We propose scaling up 3D scene reconstruction by training with synthesized data. At the core of our work is MegaSynth, a procedurally generated 3D dataset comprising 700K scenes - over 50 times larger than the prior real dataset DL3DV - dramatically scaling the training data. To enable scalable data generation, our key idea is eliminating semantic information, removing the need to model complex semantic priors such as object affordances and scene composition. Instead, we model scenes with basic spatial structures and geometry primitives, offering scalability. Besides, we control data complexity to facilitate training while loosely aligning it with real-world data distribution to benefit real-world generalization. We explore training LRMs with both MegaSynth and available real data. Experiment results show that joint training or pre-training with MegaSynth improves reconstruction quality by 1.2 to 1.8 dB PSNR across diverse image domains. Moreover, models trained solely on MegaSynth perform comparably to those trained on real data, underscoring the low-level nature of 3D reconstruction. Additionally, we provide an in-depth analysis of MegaSynth's properties for enhancing model capability, training stability, and generalization, as well as application to other tasks.

• The paper introduces MegaSynth, a large-scale synthetic dataset using basic shapes to create 700K scenes, enabling scalable 3D scene reconstruction without detailed semantic data.
• Experiments show that training Large Reconstruction Models on MegaSynth significantly improves reconstruction accuracy (1.2-1.8 dB PSNR gain) compared to models trained on real scenes.
• This work democratizes training for complex 3D models by reducing data costs and suggests that semantic cues may be less critical for 3D reconstruction than previously thought.

An Academic Insight into "MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data"

The paper "MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data" introduces a novel approach for enhancing 3D scene reconstruction by utilizing a procedurally generated dataset called MegaSynth. This dataset significantly expands the scope of training data with 700K synthetic scenes, which is substantially larger than contemporary real-world datasets, like DL3DV. The main thrust of the paper is to move beyond traditional object-level reconstruction models by optimizing scene-level data generation, eschewing complex semantic information in favor of more fundamental geometric and spatial structures. This choice underpins the scalability and extensibility of the MegaSynth dataset.

Core Contributions and Methodology

• Dataset Creation: The core contribution is the MegaSynth dataset, which employs basic shape primitives to generate a vast and scalable array of 3D scenes. Notably, these scenes do not depend on detailed semantic attributes, which simplifies their creation process while permitting greater diversity and scale.
• Data Complexity and Real-World Alignment: The authors strategically control data complexity to aid model training and align the dataset partially with real-world distributions, thereby bolstering generalization capabilities in real-world settings.
• Experimental Validation: By deploying MegaSynth in the training of Large Reconstruction Models (LRMs), the authors demonstrate a noticeable improvement in the PSNR of reconstructed scenes, ranging between 1.2 to 1.8 dB across diverse image datasets. The paper detail models trained exclusively on MegaSynth attaining performance levels comparable to those trained on real scenes, suggesting that semantic cues might be less crucial in 3D reconstruction tasks than previously assumed.

Experimental Results

The empirical results mark a significant improvement in reconstruction accuracy, highlighting the efficacy of synthesized training data over purely real-or mixed dataset approaches. Noteworthy is the dataset's provision of precise metadata, which augments model stability and performance by adding geometric supervision to the learning pipeline.

Broader Implications

The implications of this research are twofold. Practically, it democratizes the training of complex 3D scene reconstruction models by offering an accessible, scalable synthetic dataset that reduces the previously prohibitive data collection and labeling costs associated with real-world datasets. Theoretically, it challenges the presumed necessity of semantic data in 3D vision tasks, potentially influencing future research directions to explore low-level vision cues more extensively.

Future Outlook

This work opens several avenues for future research and development. One substantial direction could involve augmenting the dataset to incorporate semantic information selectively, examining its impacts on model performance given the foundational groundwork of MegaSynth. Moreover, evolving the dataset and models to address outdoor scenes or domains requiring broader contextual awareness could be beneficial. Furthermore, integrating this methodology with emerging deep learning architectures could provide insights into the interplay between data scalability and model complexity.

In summary, "MegaSynth: Scaling Up 3D Scene Reconstruction with Synthesized Data" presents a meaningful step towards revolutionizing 3D scene reconstruction through synthetic data, offering a scalable, less semantically constrained foundation that could reshape how large datasets for AI training are conceived and generated. This paper serves as both a benchmark and a catalyst for future innovations in the sphere of AI-driven 3D vision technologies.