Taming 3DGS: High-Quality Radiance Fields with Limited Resources

• URL: http://arxiv.org/abs/2406.15643v1
• Date: Fri, 21 Jun 2024 20:44:23 GMT
• Title: Taming 3DGS: High-Quality Radiance Fields with Limited Resources
• Authors: Saswat Subhajyoti Mallick, Rahul Goel, Bernhard Kerbl, Francisco Vicente Carrasco, Markus Steinberger, Fernando De La Torre,
• Abstract summary: 3D Gaussian Splatting (3DGS) has transformed novel-view synthesis with its fast, interpretable, and high-fidelity rendering. We tackle the challenges of training and rendering 3DGS models on a budget. We derive faster, numerically equivalent solutions for gradient computation and attribute updates.
• Score: 50.92437599516609
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting (3DGS) has transformed novel-view synthesis with its fast, interpretable, and high-fidelity rendering. However, its resource requirements limit its usability. Especially on constrained devices, training performance degrades quickly and often cannot complete due to excessive memory consumption of the model. The method converges with an indefinite number of Gaussians -- many of them redundant -- making rendering unnecessarily slow and preventing its usage in downstream tasks that expect fixed-size inputs. To address these issues, we tackle the challenges of training and rendering 3DGS models on a budget. We use a guided, purely constructive densification process that steers densification toward Gaussians that raise the reconstruction quality. Model size continuously increases in a controlled manner towards an exact budget, using score-based densification of Gaussians with training-time priors that measure their contribution. We further address training speed obstacles: following a careful analysis of 3DGS' original pipeline, we derive faster, numerically equivalent solutions for gradient computation and attribute updates, including an alternative parallelization for efficient backpropagation. We also propose quality-preserving approximations where suitable to reduce training time even further. Taken together, these enhancements yield a robust, scalable solution with reduced training times, lower compute and memory requirements, and high quality. Our evaluation shows that in a budgeted setting, we obtain competitive quality metrics with 3DGS while achieving a 4--5x reduction in both model size and training time. With more generous budgets, our measured quality surpasses theirs. These advances open the door for novel-view synthesis in constrained environments, e.g., mobile devices.

Related papers

• CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes [53.107474952492396]
  CityGaussianV2 is a novel approach for large-scale scene reconstruction. We implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence. Our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs.
  arXiv  Detail & Related papers  (2024-11-01T17:59:31Z)
• Trimming the Fat: Efficient Compression of 3D Gaussian Splats through Pruning [17.097742540845672]
  "Trimming the fat" is a post-hoc gradient-informed iterative pruning technique to eliminate redundant information encoded in the model. Our approach achieves around 50$times$ compression while preserving performance similar to the baseline model, and is able to speed-up computation up to 600 FPS.
  arXiv  Detail & Related papers  (2024-06-26T09:57:55Z)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled spatial sensitivity pruning score that outperforms current approaches. We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model. Our pipeline increases the average rendering speed of 3D-GS by 2.65$times$ while retaining more salient foreground information.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• LP-3DGS: Learning to Prune 3D Gaussian Splatting [71.97762528812187]
  We propose learning-to-prune 3DGS, where a trainable binary mask is applied to the importance score that can find optimal pruning ratio automatically. Experiments have shown that LP-3DGS consistently produces a good balance that is both efficient and high quality.
  arXiv  Detail & Related papers  (2024-05-29T05:58:34Z)
• DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus [56.45194233357833]
  We propose DoGaussian, a method that trains 3DGS distributedly. Our method accelerates the training of 3DGS by 6+ times when evaluated on large-scale scenes.
  arXiv  Detail & Related papers  (2024-05-22T19:17:58Z)
• EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation [29.334665494061113]
  'EfficientGS' is an advanced approach that optimize 3DGS for high-resolution, large-scale scenes. We analyze the densification process in 3DGS and identify areas of Gaussian over-proliferation. We propose a selective strategy, limiting Gaussian increase to key redundant primitives, thereby enhancing the representational efficiency.
  arXiv  Detail & Related papers  (2024-04-19T10:32:30Z)
• GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering [112.16239342037714]
  GES (Generalized Exponential Splatting) is a novel representation that employs Generalized Exponential Function (GEF) to model 3D scenes. With the aid of a frequency-modulated loss, GES achieves competitive performance in novel-view synthesis benchmarks.
  arXiv  Detail & Related papers  (2024-02-15T17:32:50Z)
• EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS [40.94643885302646]
  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 Radiance Neural Fields (NeRFs) We present a technique utilizing quantized embeddings to significantly reduce per-point memory storage requirements.
  arXiv  Detail & Related papers  (2023-12-07T18:59:55Z)

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.