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content/CSE5519/CSE5519_C4.md
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# CSE5519 Advances in Computer Vision (Topic C: 2024 - 2025: Neural Rendering)
## COLMAP-Free 3D Gaussian SplattingLinks to an external site
[link to the paper](https://arxiv.org/pdf/2312.07504)
We propose a novel 3D Gaussian Splatting (3DGS) framework that eliminates the need for COLMAP for camera pose estimation and bundle adjustment.
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> This paper presents a novel 3D Gaussian Splatting framework that eliminates the need for COLMAP for camera pose estimation and bundle adjustment.
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> Inspired by point map construction, the author uses Gaussian splatting to reconstruct the 3D scene. I wonder how this method might contribute to higher resolution reconstruction or improvements. Can we use the original COLMAP on traditional NeRF methods for comparable results?

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content/CSE5519/CSE5519_F5.md
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# CSE5519 Advances in Computer Vision (Topic F: 2025: Representation Learning)
## Can Generative Models Improve Self-Supervised Representation Learning?
[link to the paper](https://arxiv.org/pdf/2403.05966)
### Novelty in SSL with Generative Models
- Use generative models to generate synthetic data to train self-supervised representation learning models.
- Use generative augmentation to generate new data from the original data using a generative model. (with gaussian noise, or other data augmentation techniques)
- Using standard augmentation techniques like flipping, cropping, and color jittering with generative techniques can further improve the performance of the self-supervised representation learning models.
> [!TIP]
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> This paper shows that using generative models to generate synthetic data can improve the performance of self-supervised representation learning models. The key seems to be the use of generative augmentation to generate new data from the original data using a generative model.
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> However, both representation learning and generative modeling have some hallucinations. I wonder will these kinds of hallucinations would be reinforced, and the bias in the generation model would propagate to the representation learning model in the process of generative augmentation?