Remove typos and inconsistencies

README.md

@@ -600,7 +600,7 @@ We introduce GaussianAvatars, a new method to create photorealistic head avatars
 We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting. Unlike the original Gaussian Splatting or neural implicit rendering methods that necessitate per-subject optimizations, we introduce Gaussian parameter maps defined on the source views and regress directly Gaussian Splatting properties for instant novel view synthesis without any fine-tuning or optimization. To this end, we train our Gaussian parameter regression module on a large amount of human scan data, jointly with a depth estimation module to lift 2D parameter maps to 3D space. The proposed framework is fully differentiable and experiments on several datasets demonstrate that our method outperforms state-of-the-art methods while achieving an exceeding rendering speed.
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- [📄 Paper)](https://arxiv.org/pdf/2312.02155.pdf) | [🌐 Project Page](https://github.com/ShunyuanZheng/GPS-Gaussian) | [💻 Code](https://github.com/ShunyuanZheng/GPS-Gaussian) | [🎥 Short Presentation](https://youtu.be/TBIekcqt0j0)
+ [📄 Paper](https://arxiv.org/pdf/2312.02155.pdf) | [🌐 Project Page](https://github.com/ShunyuanZheng/GPS-Gaussian) | [💻 Code](https://github.com/ShunyuanZheng/GPS-Gaussian) | [🎥 Short Presentation](https://youtu.be/TBIekcqt0j0)
 
 ### 11. GauHuman: Articulated Gaussian Splatting from Monocular Human Videos 
 **Authors**: Shoukang Hu Ziwei Liu  
@@ -865,7 +865,7 @@ In summary, LightGaussian achieves an averaged compression rate over 15x while b
 Neural Radiance Fields (NeRFs) have demonstrated remarkable potential in capturing complex 3D scenes with high fidelity. However, one persistent challenge that hinders the widespread adoption of NeRFs is the computational bottleneck due to the volumetric rendering. On the other hand, 3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussisan-based representation and adopts the rasterization pipeline to render the images rather than volumetric rendering, achieving very fast rendering speed and promising image quality. However, a significant drawback arises as 3DGS entails a substantial number of 3D Gaussians to maintain the high fidelity of the rendered images, which requires a large amount of memory and storage. To address this critical issue, we place a specific emphasis on two key objectives: reducing the number of Gaussian points without sacrificing performance and compressing the Gaussian attributes, such as view-dependent color and covariance. To this end, we propose a learnable mask strategy that significantly reduces the number of Gaussians while preserving high performance. In addition, we propose a compact but effective representation of view-dependent color by employing a grid-based neural field rather than relying on spherical harmonics. Finally, we learn codebooks to compactly represent the geometric attributes of Gaussian by vector quantization. In our extensive experiments, we consistently show over 10× reduced storage and enhanced rendering speed, while maintaining the quality of the scene representation, compared to 3DGS. Our work provides a comprehensive framework for 3D scene representation, achieving high performance, fast training, compactness, and real-time rendering.
 </details>
 
-  [📄 Paper](https://arxiv.org/pdf/2311.13681.pdf) | [🌐 Project Page](https://maincold2.github.io/c3dgs/) | [💻 Code ](https://github.com/maincold2/Compact-3DGS) 
+  [📄 Paper](https://arxiv.org/pdf/2311.13681.pdf) | [🌐 Project Page](https://maincold2.github.io/c3dgs/) | [💻 Code](https://github.com/maincold2/Compact-3DGS) 
 
 ### 4. Compact 3D Scene Representation via Self-Organizing Gaussian Grids 
 **Authors**: Wieland Morgenstern, Florian Barthel, Anna Hilsmann, Peter Eisert 
@@ -1383,7 +1383,7 @@ Novel view synthesis of dynamic scenes has been an intriguing yet challenging pr
 Accurate 3D tracking in highly deformable scenes with occlusions and shadows can facilitate new applications in robotics, augmented reality, and generative AI. However, tracking under these conditions is extremely challenging due to the ambiguity that arises with large deformations, shadows, and occlusions. We introduce MD-Splatting, an approach for simultaneous 3D tracking and novel view synthesis, using video captures of a dynamic scene from various camera poses. MD-Splatting builds on recent advances in Gaussian splatting, a method that learns the properties of a large number of Gaussians for state-of-the-art and fast novel view synthesis. MD-Splatting learns a deformation function to project a set of Gaussians with non-metric, thus canonical, properties into metric space. The deformation function uses a neural-voxel encoding and a multilayer perceptron (MLP) to infer Gaussian position, rotation, and a shadow scalar. We enforce physics-inspired regularization terms based on local rigidity, conservation of momentum, and isometry, which leads to trajectories with smaller trajectory errors. MD-Splatting achieves high-quality 3D tracking on highly deformable scenes with shadows and occlusions. Compared to state-of-the-art, we improve 3D tracking by an average of 23.9 %, while simultaneously achieving high-quality novel view synthesis. With sufficient texture such as in scene 6, MD-Splatting achieves a median tracking error of 3.39 mm on a cloth of 1 x 1 meters in size
 </details>
 
-  [📄 Paper](https://arxiv.org/pdf/2312.00583) | [🌐 Project Page](https://md-splatting.github.io/) | [💻 Code (not released yet)](https://github.com/momentum-robotics-lab/md-splatting) 
+  [📄 Paper](https://arxiv.org/pdf/2312.00583) | [🌐 Project Page](https://md-splatting.github.io/) | [💻 Code (not yet)](https://github.com/momentum-robotics-lab/md-splatting) 
 
 ### 15. SWAGS: Sampling Windows Adaptively for Dynamic 3D Gaussian Splatting 
 **Authors**: Richard Shaw, Jifei Song, Arthur Moreau, Michal Nazarczuk, Sibi Catley-Chandar, Helisa Dhamo, Eduardo Perez-Pellitero 
@@ -1604,7 +1604,7 @@ Precisely perceiving the geometric and semantic properties of real-world 3D obje
 We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS. Leveraging the coherence of the Gaussian splatting and position-based dynamics (PBD) in the underlying representation, we manage rendering, view synthesis, and the dynamics of solids and fluids in a cohesive manner. Similar to Gaussian shader, we enhance each Gaussian kernel with an added normal, aligning the kernel's orientation with the surface normal to refine the PBD simulation. This approach effectively eliminates spiky noises that arise from rotational deformation in solids. It also allows us to integrate physically based rendering to augment the dynamic surface reflections on fluids. Consequently, our framework is capable of realistically reproducing surface highlights on dynamic fluids and facilitating interactions between scene objects and fluids from new views. 
 </details>
 
-  [📄 Paper](https://browse.arxiv.org/pdf/2401.15318.pdf) | [🌐 Project Page](https://amysteriouscat.github.io/GaussianSplashing/) | [💻 Code (not released yet)]() | [🎥 Short Presentation](https://www.youtube.com/watch?v=KgaR1ni-Egg&t)
+  [📄 Paper](https://browse.arxiv.org/pdf/2401.15318.pdf) | [🌐 Project Page](https://amysteriouscat.github.io/GaussianSplashing/) | [💻 Code (not yet)]() | [🎥 Short Presentation](https://www.youtube.com/watch?v=KgaR1ni-Egg&t)
 
 ### 2. GaMeS: Mesh-Based Adapting and Modification of Gaussian Splatting 
 **Authors**: Joanna Waczyńska, Piotr Borycki, Sławomir Tadeja, Jacek Tabor, Przemysław Spurek 
@@ -1936,7 +1936,7 @@ Differentiable rendering is a technique used in an important emerging class of v
 3D Gaussian splatting (3DGS) has recently demonstrated impressive capabilities in real-time novel view synthesis and 3D reconstruction. However, 3DGS heavily depends on the accurate initialization derived from Structure-from-Motion (SfM) methods. When trained with randomly initialized point clouds, 3DGS often fails to maintain its ability to produce high-quality images, undergoing large performance drops of 4-5 dB in PSNR in general. Through extensive analysis of SfM initialization in the frequency domain and analysis of a 1D regression task with multiple 1D Gaussians, we propose a novel optimization strategy dubbed RAIN-GS (Relaxing Accurate INitialization Constraint for 3D Gaussian Splatting) that successfully trains 3D Gaussians from randomly initialized point clouds. We show the effectiveness of our strategy through quantitative and qualitative comparisons on standard datasets, largely improving the performance in all settings. 
 </details>
 
-  [📄 Paper](https://arxiv.org/pdf/2403.09413) | [🌐 Project Page](https://ku-cvlab.github.io/RAIN-GS/) | [💻 Code ](https://github.com/KU-CVLAB/RAIN-GS) 
+  [📄 Paper](https://arxiv.org/pdf/2403.09413) | [🌐 Project Page](https://ku-cvlab.github.io/RAIN-GS/) | [💻 Code](https://github.com/KU-CVLAB/RAIN-GS) 
 
 ### 4. A New Split Algorithm for 3D Gaussian Splatting 
 **Authors**: Qiyuan Feng, Gengchen Cao, Haoxiang Chen, Tai-Jiang Mu, Ralph R. Martin, Shi-Min Hu 
@@ -1955,7 +1955,7 @@ Differentiable rendering is a technique used in an important emerging class of v
 In this paper, we present a method to optimize Gaussian splatting with a limited number of images while avoiding overfitting. Representing a 3D scene by combining numerous Gaussian splats has yielded outstanding visual quality. However, it tends to overfit the training views when only a small number of images are available. To address this issue, we introduce a dense depth map as a geometry guide to mitigate overfitting. We obtained the depth map using a pre-trained monocular depth estimation model and aligning the scale and offset using sparse COLMAP feature points. The adjusted depth aids in the color-based optimization of 3D Gaussian splatting, mitigating floating artifacts, and ensuring adherence to geometric constraints. We verify the proposed method on the NeRF-LLFF dataset with varying numbers of few images. Our approach demonstrates robust geometry compared to the original method that relies solely on images. 
 </details>
 
-  [📄 Paper](https://arxiv.org/pdf/2311.13398.pdf) | [🌐 Project Page](https://robot0321.github.io/DepthRegGS/index.html) | [💻 Code ](https://github.com/robot0321/DepthRegularizedGS) 
+  [📄 Paper](https://arxiv.org/pdf/2311.13398.pdf) | [🌐 Project Page](https://robot0321.github.io/DepthRegGS/index.html) | [💻 Code](https://github.com/robot0321/DepthRegularizedGS) 
 
 ### 2. EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS 
 **Authors**: Sharath Girish, Kamal Gupta, Abhinav Shrivastava 
@@ -1964,7 +1964,7 @@ In this paper, we present a method to optimize Gaussian splatting with a limited
 Recently, 3D Gaussian splatting (3D-GS) has gained popularity in novel-view scene synthesis. It addresses the challenges of lengthy training times and slow rendering speeds associated with Neural Radiance Fields (NeRFs). Through rapid, differentiable rasterization of 3D Gaussians, 3D-GS achieves real-time rendering and accelerated training. They, however, demand substantial memory resources for both training and storage, as they require millions of Gaussians in their point cloud representation for each scene. We present a technique utilizing quantized embeddings to significantly reduce memory storage requirements and a coarse-to-fine training strategy for a faster and more stable optimization of the Gaussian point clouds. Our approach results in scene representations with fewer Gaussians and quantized representations, leading to faster training times and rendering speeds for real-time rendering of high resolution scenes. We reduce memory by more than an order of magnitude all while maintaining the reconstruction quality. We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20x less memory and faster training/inference speed. 
 </details>
 
-  [📄 Paper](https://arxiv.org/pdf/2312.04564.pdf) | [🌐 Project Page](https://efficientgaussian.github.io/) | [💻 Code ](https://github.com/Sharath-girish/efficientgaussian) 
+  [📄 Paper](https://arxiv.org/pdf/2312.04564.pdf) | [🌐 Project Page](https://efficientgaussian.github.io/) | [💻 Code](https://github.com/Sharath-girish/efficientgaussian) 
 
 <br>
 
@@ -2305,7 +2305,7 @@ https://github.com/maturk/dn-splatter
 Recent studies in Radiance Fields have paved the robust way for novel view synthesis with their photorealistic rendering quality. Nevertheless, they usually employ neural networks and volumetric rendering, which are costly to train and impede their broad use in various real-time applications due to the lengthy rendering time. Lately 3D Gaussians splatting-based approach has been proposed to model the 3D scene, and it achieves remarkable visual quality while rendering the images in real-time. However, it suffers from severe degradation in the rendering quality if the training images are blurry. Blurriness commonly occurs due to the lens defocusing, object motion, and camera shake, and it inevitably intervenes in clean image acquisition. Several previous studies have attempted to render clean and sharp images from blurry input images using neural fields. The majority of those works, however, are designed only for volumetric rendering-based neural radiance fields and are not straightforwardly applicable to rasterization-based 3D Gaussian splatting methods. Thus, we propose a novel real-time deblurring framework, deblurring 3D Gaussian Splatting, using a small Multi-Layer Perceptron (MLP) that manipulates the covariance of each 3D Gaussian to model the scene blurriness. While deblurring 3D Gaussian Splatting can still enjoy real-time rendering, it can reconstruct fine and sharp details from blurry images. A variety of experiments have been conducted on the benchmark, and the results have revealed the effectiveness of our approach for deblurring.
 </details>
 
-  [📄 Paper)](https://arxiv.org/pdf/2401.00834.pdf) | [🌐 Project Page](https://benhenryl.github.io/Deblurring-3D-Gaussian-Splatting/) | [💻 Code (not yet)](https://github.com/benhenryL/Deblurring-3D-Gaussian-Splatting) 
+  [📄 Paper](https://arxiv.org/pdf/2401.00834.pdf) | [🌐 Project Page](https://benhenryl.github.io/Deblurring-3D-Gaussian-Splatting/) | [💻 Code (not yet)](https://github.com/benhenryL/Deblurring-3D-Gaussian-Splatting) 
 
 ### 8. GIR: 3D Gaussian Inverse Rendering for Relightable Scene Factorization
 **Authors**: Yahao Shi, Yanmin Wu, Chenming Wu, Xing Liu, Chen Zhao, Haocheng Feng, Jingtuo Liu, Liangjun Zhang, Jian Zhang, Bin Zhou, Errui Ding, Jingdong Wang
@@ -2314,7 +2314,7 @@ Recent studies in Radiance Fields have paved the robust way for novel view synth
 This paper presents GIR, a 3D Gaussian Inverse Rendering method for relightable scene factorization. Compared to existing methods leveraging discrete meshes or neural implicit fields for inverse rendering, our method utilizes 3D Gaussians to estimate the material properties, illumination, and geometry of an object from multi-view images. Our study is motivated by the evidence showing that 3D Gaussian is a more promising backbone than neural fields in terms of performance, versatility, and efficiency. In this paper, we aim to answer the question: "How can 3D Gaussian be applied to improve the performance of inverse rendering?" To address the complexity of estimating normals based on discrete and often in-homogeneous distributed 3D Gaussian representations, we proposed an efficient self-regularization method that facilitates the modeling of surface normals without the need for additional supervision. To reconstruct indirect illumination, we propose an approach that simulates ray tracing. Extensive experiments demonstrate our proposed GIR's superior performance over existing methods across multiple tasks on a variety of widely used datasets in inverse rendering. This substantiates its efficacy and broad applicability, highlighting its potential as an influential tool in relighting and reconstruction.
 </details>
 
- [📄 Paper](https://arxiv.org/pdf/2312.05133) | [🌐 Project Page](https://3dgir.github.io/) | [💻 Code(not yet)]() 
+ [📄 Paper](https://arxiv.org/pdf/2312.05133) | [🌐 Project Page](https://3dgir.github.io/) | [💻 Code (not yet)]() 
 
 ### 9. Gaussian Splatting with NeRF-based Color and Opacity
 **Authors**: Dawid Malarz, Weronika Smolak, Jacek Tabor, Sławomir Tadeja, Przemysław Spurek