Fully Explicit Dynamic Gaussian Splatting
- URL: http://arxiv.org/abs/2410.15629v2
- Date: Tue, 22 Oct 2024 12:02:29 GMT
- Title: Fully Explicit Dynamic Gaussian Splatting
- Authors: Junoh Lee, Chang-Yeon Won, Hyunjun Jung, Inhwan Bae, Hae-Gon Jeon,
- Abstract summary: 3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations.
We introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS's convergence.
Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU.
- Score: 22.889981393105554
- License:
- Abstract: 3D Gaussian Splatting has shown fast and high-quality rendering results in static scenes by leveraging dense 3D prior and explicit representations. Unfortunately, the benefits of the prior and representation do not involve novel view synthesis for dynamic motions. Ironically, this is because the main barrier is the reliance on them, which requires increasing training and rendering times to account for dynamic motions. In this paper, we design a Explicit 4D Gaussian Splatting(Ex4DGS). Our key idea is to firstly separate static and dynamic Gaussians during training, and to explicitly sample positions and rotations of the dynamic Gaussians at sparse timestamps. The sampled positions and rotations are then interpolated to represent both spatially and temporally continuous motions of objects in dynamic scenes as well as reducing computational cost. Additionally, we introduce a progressive training scheme and a point-backtracking technique that improves Ex4DGS's convergence. We initially train Ex4DGS using short timestamps and progressively extend timestamps, which makes it work well with a few point clouds. The point-backtracking is used to quantify the cumulative error of each Gaussian over time, enabling the detection and removal of erroneous Gaussians in dynamic scenes. Comprehensive experiments on various scenes demonstrate the state-of-the-art rendering quality from our method, achieving fast rendering of 62 fps on a single 2080Ti GPU.
Related papers
- 4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenes [19.24815625343669]
SaRO-GS is a novel dynamic scene representation capable of achieving real-time rendering.
To handle temporally complex dynamic scenes, we introduce a Scale-aware Residual Field.
Our method has demonstrated state-of-the-art performance.
arXiv Detail & Related papers (2024-12-09T08:44:19Z) - Temporally Compressed 3D Gaussian Splatting for Dynamic Scenes [46.64784407920817]
Temporally Compressed 3D Gaussian Splatting (TC3DGS) is a novel technique designed specifically to compress dynamic 3D Gaussian representations.
Our experiments across multiple datasets demonstrate that TC3DGS achieves up to 67$times$ compression with minimal or no degradation in visual quality.
arXiv Detail & Related papers (2024-12-07T17:03:09Z) - Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives [60.217580865237835]
3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differentiable 3D Gaussians.
We identify and address two key inefficiencies in 3D-GS, achieving substantial improvements in rendering speed, model size, and training time.
Our Speedy-Splat approach combines these techniques to accelerate average rendering speed by a drastic $6.71times$ across scenes from the Mip-NeRF 360, Tanks & Temples, and Deep Blending datasets with $10.6times$ fewer primitives than 3
arXiv Detail & Related papers (2024-11-30T20:25:56Z) - PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.
We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
arXiv Detail & Related papers (2024-06-14T17:53:55Z) - 4D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes [33.14021987166436]
We introduce 4DRotorGS, a novel method that represents dynamic scenes with anisotropic 4D XYZT Gaussians.
As an explicit spatial-temporal representation, 4DRotorGS demonstrates powerful capabilities for modeling complicated dynamics and fine details.
We further implement our temporal slicing and acceleration framework, achieving real-time rendering speeds of up to 277 FPS on an 3090 GPU and 583 FPS on a 4090 GPU.
arXiv Detail & Related papers (2024-02-05T18:59:04Z) - Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis [28.455719771979876]
We propose Spacetime Gaussian Feature Splatting as a novel dynamic scene representation.
Our method achieves state-of-the-art rendering quality and speed, while retaining compact storage.
arXiv Detail & Related papers (2023-12-28T04:14:55Z) - Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering [71.44349029439944]
Recent 3D Gaussian Splatting method has achieved the state-of-the-art rendering quality and speed.
We introduce Scaffold-GS, which uses anchor points to distribute local 3D Gaussians.
We show that our method effectively reduces redundant Gaussians while delivering high-quality rendering.
arXiv Detail & Related papers (2023-11-30T17:58:57Z) - 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering [103.32717396287751]
We propose 4D Gaussian Splatting (4D-GS) as a holistic representation for dynamic scenes.
A neuralvoxel encoding algorithm inspired by HexPlane is proposed to efficiently build features from 4D neural voxels.
Our 4D-GS method achieves real-time rendering under high resolutions, 82 FPS at an 800$times$800 resolution on an 3090 GPU.
arXiv Detail & Related papers (2023-10-12T17:21:41Z) - Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene
Reconstruction [29.83056271799794]
Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering.
We propose a deformable 3D Gaussians Splatting method that reconstructs scenes using 3D Gaussians and learns them in canonical space.
Through a differential Gaussianizer, the deformable 3D Gaussians not only achieve higher rendering quality but also real-time rendering speed.
arXiv Detail & Related papers (2023-09-22T16:04:02Z) - Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis [58.5779956899918]
We present a method that simultaneously addresses the tasks of dynamic scene novel-view synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements.
We follow an analysis-by-synthesis framework, inspired by recent work that models scenes as a collection of 3D Gaussians.
We demonstrate a large number of downstream applications enabled by our representation, including first-person view synthesis, dynamic compositional scene synthesis, and 4D video editing.
arXiv Detail & Related papers (2023-08-18T17:59:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.