MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction
On the map: Diffusion models → 3D, point cloud and scene generation
Abstract (arXiv)
This paper presents a neural architecture MVDiffusion++ for 3D object reconstruction that synthesizes dense and high-resolution views of an object given one or a few images without camera poses. MVDiffusion++ achieves superior flexibility and scalability with two surprisingly simple ideas: 1) A ``pose-free architecture'' where standard self-attention among 2D latent features learns 3D consistency across an arbitrary number of conditional and generation views without explicitly using camera pose information; and 2) A ``view dropout strategy'' that discards a substantial number of output views during training, which reduces the training-time memory footprint and enables dense and high-resolution view synthesis at test time. We use the Objaverse for training and the Google Scanned Objects for evaluation with standard novel view synthesis and 3D reconstruction metrics, where MVDiffusion++ significantly outperforms the current state of the arts. We also demonstrate a text-to-3D application example by combining MVDiffusion++ with a text-to-image generative model. The project page is at https://mvdiffusion-plusplus.github.io.
Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 38 of its 46 references that are in the map. First posted on arXiv 20 Feb 2024.
Builds on
38 of 46 references.
Closest ancestors · 10
Same direction, newest first
- 2023ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation2 Dec
- 2023One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion14 Nov
- 2023Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model23 Oct
- 2023Wonder3D: Single Image to 3D using Cross-Domain Diffusion23 Oct
- 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Image7 Sep
- 2023MVDream: Multi-view Diffusion for 3D Generation31 Aug
- 2023Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors30 Jun
- 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization29 Jun
- 2023Zero-1-to-3: Zero-shot One Image to 3D Object20 Mar
- 2022Point-E: A System for Generating 3D Point Clouds from Complex Prompts16 Dec
Other references · 28
- 2017Attention Is All You Needcited by 59,932
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
- 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,611
- 2023FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioningcited by 2,551
- 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
- 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,522
- 2023LRM: Large Reconstruction Model for Single Image to 3Dcited by 834
- 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 780
- 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 601
16 more
- 2022MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstructioncited by 467
- 2023Neuralangelo: High-Fidelity Neural Surface Reconstructioncited by 464
- 2023Shap-E: Generating Conditional 3D Implicit Functionscited by 379
- 2021Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scanscited by 336
- 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 324
- 2023Common Diffusion Noise Schedules and Sample Steps are Flawedcited by 308
- 2023RealFusion: 360deg Reconstruction of Any Object from a Single Imagecited by 305
- 2023DMV3D: Denoising Multi-View Diffusion using 3D Large Reconstruction Modelcited by 213
- 2023MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusioncited by 210
- 2022AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generationcited by 208
- 2023PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Predictioncited by 165
- 2022NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priorscited by 125
- 2016Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervisioncited by 106
- 2023LEAP: Liberate Sparse-view 3D Modeling from Camera Posescited by 65
- 2022FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstructioncited by 19
- 2023Slice3D: Multi-Slice, Occlusion-Revealing, Single View 3D Reconstructioncited by 13
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Citing papers per year: 2024: 44; 2025: 37; 2026: 13.
94 papers.
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