GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
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Cited by 58 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from sparse-view images in around 0.1s. GRM is a feed-forward transformer-based model that efficiently incorporates multi-view information to translate the input pixels into pixel-aligned Gaussians, which are unprojected to create a set of densely distributed 3D Gaussians representing a scene. Together, our transformer architecture and the use of 3D Gaussians unlock a scalable and efficient reconstruction framework. Extensive experimental results demonstrate the superiority of our method over alternatives regarding both reconstruction quality and efficiency. We also showcase the potential of GRM in generative tasks, i.e., text-to-3D and image-to-3D, by integrating it with existing multi-view diffusion models. Our project website is at: https://justimyhxu.github.io/projects/grm/.
Key facts. Cited by 299 papers in the map (data through 13 Oct 2026). Builds on 103 of its 111 references that are in the map. First posted on arXiv 21 Mar 2024.
Builds on
103 of 111 references.
References · 103
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- 2023Segment Anythingcited by 8,195
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- 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,712
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- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,975
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,687
- 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,686
- 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,225
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- 2023ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillationcited by 1,212
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- 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
- 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 700
- 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 697
- 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
- 2023Wonder3D: Single Image to 3D using Cross-Domain Diffusioncited by 686
- 2023Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creationcited by 685
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- 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Imagecited by 640
- 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 605
- 2022Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generationcited by 597
- 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 557
50 most cited of 103 All 103 (JSON)
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Citing papers per year: 2024: 102; 2025: 127; 2026: 70.
299 papers.
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