https://github.com/KwanWaiPang/Awesome-Diffusion-based-SLAM
Paper Survey for Diffusion-based SLAM
https://github.com/KwanWaiPang/Awesome-Diffusion-based-SLAM
List: Awesome-Diffusion-based-SLAM
awesome deep-learning depth-estimation diffusion-models pose-estimation slam
Last synced: about 2 months ago
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Paper Survey for Diffusion-based SLAM
- Host: GitHub
- URL: https://github.com/KwanWaiPang/Awesome-Diffusion-based-SLAM
- Owner: KwanWaiPang
- Created: 2025-03-20T07:20:21.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-07T08:43:49.000Z (about 2 months ago)
- Last Synced: 2025-04-07T09:34:56.929Z (about 2 months ago)
- Topics: awesome, deep-learning, depth-estimation, diffusion-models, pose-estimation, slam
- Homepage: https://kwanwaipang.github.io/Diffusion_SLAM/
- Size: 43.9 KB
- Stars: 5
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Learning-based-VO-VIO - Paper List
- ultimate-awesome - Awesome-Diffusion-based-SLAM - Paper Survey for Diffusion-based SLAM. (Other Lists / Julia Lists)
- awesome-3dgs-slam - Paper List
README
Awesome Diffusion-based SLAM
This repository contains a curated list of resources addressing SLAM related task employing Diffusion Model, including view/feature correspondences, depth estimation, 3D reconstruction, pose estimation, etc.
If you find some ignored papers, **feel free to [*create pull requests*](https://github.com/KwanWaiPang/Awesome-Transformer-based-SLAM/blob/pdf/How-to-PR.md), or [*open issues*](https://github.com/KwanWaiPang/Awesome-Diffusion-based-SLAM/issues/new)**.
Contributions in any form to make this list more comprehensive are welcome.
If you find this repositorie is useful, a simple star should be the best affirmation. 😊
Feel free to share this list with others!
# Overview
- [Matching](#Matching)
- [Depth Estimation](#Depth-Estimation)
- [Pose Estimation](#Pose-Estimation)
- [Other Resource](#Other-Resource)
## Matching
or data association, or correspondence
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2025|`arXiv`|[MATCHA: Towards Matching Anything](https://arxiv.org/pdf/2501.14945)|---|SD+DINOv2|
|2024|`CVPR`|[Sd4match: Learning to prompt stable diffusion model for semantic matching](https://openaccess.thecvf.com/content/CVPR2024/papers/Li_SD4Match_Learning_to_Prompt_Stable_Diffusion_Model_for_Semantic_Matching_CVPR_2024_paper.pdf)|[](https://github.com/ActiveVisionLab/SD4Match)|[website](https://sd4match.active.vision/)|
|2023|`NIPS`|[Emergent correspondence from image diffusion](https://proceedings.neurips.cc/paper_files/paper/2023/file/0503f5dce343a1d06d16ba103dd52db1-Paper-Conference.pdf)|[](https://github.com/Tsingularity/dift)|[website](https://diffusionfeatures.github.io/)
DIFT|
|2023|`NIPS`|[A tale of two features: Stable diffusion complements dino for zero-shot semantic correspondence](https://proceedings.neurips.cc/paper_files/paper/2023/file/8e9bdc23f169a05ea9b72ccef4574551-Paper-Conference.pdf)|[](https://github.com/Junyi42/sd-dino)|[website](https://sd-complements-dino.github.io/)
SD+DINO|
|2023|`NIPS`|[Diffusion hyperfeatures: Searching through time and space for semantic correspondence](https://proceedings.neurips.cc/paper_files/paper/2023/file/942032b61720a3fd64897efe46237c81-Paper-Conference.pdf)|[](https://github.com/diffusion-hyperfeatures/diffusion_hyperfeatures)|[website](https://diffusion-hyperfeatures.github.io/)
DHF|
## Depth Estimation
or 3D reconstruction
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2025|`arXiv`|[Scene Splatter: Momentum 3D Scene Generation from Single Image with Video Diffusion Model](https://arxiv.org/pdf/2504.02764)|---|[website](https://shengjun-zhang.github.io/SceneSplatter/)|
|2025|`arXiv`|[Can Video Diffusion Model Reconstruct 4D Geometry ?](https://arxiv.org/pdf/2503.21082)|---|[website](https://wayne-mai.github.io/publication/sora3r_arxiv_2025/)
Sora3R|
|2025|`arXiv`|[Bolt3D: Generating 3D Scenes in Seconds](https://arxiv.org/pdf/2503.14445)|---|[website](https://szymanowiczs.github.io/bolt3d)|
|2025|`arXiv`|[Stable Virtual Camera: Generative View Synthesis with Diffusion Models](https://arxiv.org/pdf/2503.14489)|[](https://github.com/Stability-AI/stable-virtual-camera) |---|
|2025|`CVPR`|[Free360: Layered Gaussian Splatting for Unbounded 360-Degree View Synthesis from Extremely Sparse and Unposed Views](https://arxiv.org/pdf/2503.24382)|[](https://github.com/chobao/Free360)|[website](https://zju3dv.github.io/free360/)|
|2025|`CVPR`|[GenFusion: Closing the Loop between Reconstruction and Generation via Videos](https://arxiv.org/pdf/2503.21219)|[](https://github.com/Inception3D/GenFusion)|[website](https://genfusion.sibowu.com/)|
|2025|`CVPR`|[Learning temporally consistent video depth from video diffusion priors](https://arxiv.org/pdf/2406.01493)|[](https://github.com/jiahao-shao1/ChronoDepth)|[website](https://xdimlab.github.io/ChronoDepth/)|
|2025|`CVPR`|[Depthcrafter: Generating consistent long depth sequences for open-world videos](https://arxiv.org/pdf/2409.02095)|[](https://github.com/Tencent/DepthCrafter)|[website](https://depthcrafter.github.io/)|
|2025|`CVPR`|[Multi-view Reconstruction via SfM-guided Monocular Depth Estimation](https://arxiv.org/pdf/2503.14483)|[](https://github.com/zju3dv/Murre)|[website](https://zju3dv.github.io/murre/)
Murre|
|2025|`CVPR`|[Difix3D+: Improving 3D Reconstructions with Single-Step Diffusion Models](https://arxiv.org/pdf/2503.01774?)|---|[website](https://research.nvidia.com/labs/toronto-ai/difix3d/)|
|2025|`CVPR`|[Align3r: Aligned monocular depth estimation for dynamic videos](https://arxiv.org/pdf/2412.03079)|[](https://github.com/jiah-cloud/Align3R)|---|
|2024|`NIPS`|[Cat3d: Create anything in 3d with multi-view diffusion models](https://arxiv.org/pdf/2405.10314)|---|[website](https://cat3d.github.io/)|
|2024|`CVPR`|[Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation](https://openaccess.thecvf.com/content/CVPR2024/papers/Ke_Repurposing_Diffusion-Based_Image_Generators_for_Monocular_Depth_Estimation_CVPR_2024_paper.pdf)| [](https://github.com/prs-eth/marigold)|[website](https://marigoldmonodepth.github.io/)|
|2024|`ECCV`|[Diffusiondepth: Diffusion denoising approach for monocular depth estimation](https://arxiv.org/pdf/2303.05021)|[](https://github.com/duanyiqun/DiffusionDepth)|[website](https://igl-hkust.github.io/Align3R.github.io/)|
|2024|`arXiv`|[World-consistent Video Diffusion with Explicit 3D Modeling](https://arxiv.org/pdf/2412.01821)|---|[website](https://zqh0253.github.io/wvd/)|
|2023|`NIPS`|[The surprising effectiveness of diffusion models for optical flow and monocular depth estimation](https://proceedings.neurips.cc/paper_files/paper/2023/file/7c119415672ae2186e17d492e1d5da2f-Paper-Conference.pdf)|---|[website](https://diffusion-vision.github.io/)|
|2023|`arXiv`|[Monocular depth estimation using diffusion models](https://arxiv.org/pdf/2302.14816)|---|[website](https://depth-gen.github.io/)|
|2023|`arXiv`|[Mvdream: Multi-view diffusion for 3d generation](https://arxiv.org/pdf/2308.16512)|[](https://github.com/bytedance/MVDream)|[website](https://mv-dream.github.io/)|
## Pose Estimation
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2025|`arXiv`|[GCRayDiffusion: Pose-Free Surface Reconstruction via Geometric Consistent Ray Diffusion](https://arxiv.org/pdf/2503.22349)|---|---|
|2023|`ICCV`|[Posediffusion: Solving pose estimation via diffusion-aided bundle adjustment](https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_PoseDiffusion_Solving_Pose_Estimation_via_Diffusion-aided_Bundle_Adjustment_ICCV_2023_paper.pdf)|[](https://github.com/facebookresearch/PoseDiffusion)|[website](https://posediffusion.github.io/)|
## Other Resource
* Survey for Learning-based VO,VIO,IO：[Paper List](https://github.com/KwanWaiPang/Awesome-Learning-based-VO-VIO)
* Survey for Transformer-based SLAM：[Paper List](https://github.com/KwanWaiPang/Awesome-Transformer-based-SLAM)
* [Awesome-Diffusion-Models](https://github.com/diff-usion/Awesome-Diffusion-Models)
* Some basic paper for Diffusion Model:
| Year | Venue | Paper Title | Repository | Note |
|:----:|:-----:| ----------- |:----------:|:----:|
|2022|`CVPR`|[High-resolution image synthesis with latent diffusion models](https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf)|[](https://github.com/CompVis/latent-diffusion)|stable diffusion|
|2021|`NIPS`|[Diffusion models beat gans on image synthesis](https://proceedings.neurips.cc/paper_files/paper/2021/file/49ad23d1ec9fa4bd8d77d02681df5cfa-Paper.pdf)|---|Ablated Diffusion Model(ADM)|
|2020|`ICLR`|[Denoising diffusion implicit models](https://arxiv.org/pdf/2010.02502)|---|DDIM|
|2020|`NIPS`|[Denoising diffusion probabilistic models](https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf)|[](https://github.com/hojonathanho/diffusion)|DDPM|