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Octree-GS: Towards Consistent Real-time Rendering with LOD-Structured 3D Gaussians

Kerui Ren, Lihan Jiang, Tao Lu, Mulin Yu, Linning Xu, Zhangkai Ni, Bo Dai

arXiv:2403.17898v2cs.CV

TL;DR

Large, detailed scenes expose 3D-GS bottlenecks because excessive view-frustum primitives cause zoom-out inefficiency and inconsistent rendering speeds. Octree-GS introduces hierarchical LOD anchors with view- and complexity-aware selection, preserving detail while reducing rendered primitives. The paper reports improved visual performance and rendering speed across varied scenes, while retaining real-time rendering.

  • Problem

    3D-GS struggles with redundant primitives, inconsistent rendering speeds in large zoom-out scenes, and detail representation across scales.

  • Method

    Octree-GS organizes Gaussian anchors in an explicit octree, uses progressive training with grow-and-prune operations, and dynamically selects LODs according to views and scene complexity.

  • Results

    Octree-GS preserves fine-scale details while reducing Gaussian counts, rendering time, and storage overhead across indoor, outdoor, synthetic, real-world, and urban scenes.

  • Takeaways & Limitations

    The LOD-structured representation provides consistent rendering performance with adaptive detail selection while maintaining high-fidelity visual results.

  • Takeaways & Limitations

    Octree construction and progressive training require hyperparameter tuning, while the model remains dependent on precise camera poses and lacks geometry support.

Abstract

from arXiv · show

The recent 3D Gaussian splatting (3D-GS) has shown remarkable rendering fidelity and efficiency compared to NeRF-based neural scene representations. While demonstrating the potential for real-time rendering, 3D-GS encounters rendering bottlenecks in large scenes with complex details due to an excessive number of Gaussian primitives located within the viewing frustum. This limitation is particularly noticeable in zoom-out views and can lead to inconsistent rendering speeds in scenes with varying details. Moreover, it often struggles to capture the corresponding level of details at different scales with its heuristic density control operation. Inspired by the Level-of-Detail (LOD) techniques, we introduce Octree-GS, featuring an LOD-structured 3D Gaussian approach supporting level-of-detail decomposition for scene representation that contributes to the final rendering results. Our model dynamically selects the appropriate level from the set of multi-resolution anchor points, ensuring consistent rendering performance with adaptive LOD adjustments while maintaining high-fidelity rendering results.

I. INTRODUCTION

3D-GS can become inefficient and inconsistent on large, complex scenes because visibility-based selection renders excessive primitives without adapting detail to scale. Octree-GS organizes Gaussian anchors hierarchically and dynamically selects LODs to preserve quality while stabilizing rendering efficiency.

  • 3D-GS places redundant, overlapping primitives according to training-view fit rather than scene structure, weakening generalization and efficiency.
  • Visibility-based filtering renders every primitive inside the view frustum, causing redundant computation and inconsistent speeds during zoom-out trajectories.
  • Octree-GS organizes Gaussian anchors in hierarchical grids and selects LOD levels from observation footprint and scene-detail richness.
  • ≥30 FPS: Octree-GS consistently achieves real-time rendering during MatrixCity zoom-out views where Hierarchical-GS and Scaffold-GS fail to render distant primitives in real time.
  • Octree-GS achieves LOD effects in one training round and supports explicit and neural Gaussian representations while reducing training time and storage overhead.

II. RELATED WORK

Prior novel-view-synthesis methods use structured representations and LOD ideas to improve speed, storage, or scene complexity handling, but Gaussian methods still face resource costs in rich large scenes. Octree-GS applies a sparse octree to Gaussian primitives for LOD-aware rendering and stable efficiency.

  • A. Novel View Synthesis: NeRF methods achieve photorealistic rendering but often require slow stochastic sampling and deep-network computation.
  • A. Novel View Synthesis: 3D-GS improves rendering quality and speed, yet resource costs hinder stable real-time performance for rich content on computation-restricted devices.
  • Spatial Structures: Prior scene representations use voxel grids, point clouds, low-rank tensors, and hash tables to improve training, inference speed, or storage efficiency.
  • Spatial Structures: Octree-GS introduces a sparse octree structure for Gaussian primitives to improve real-time rendering stability across trajectory changes.
  • Level-of-Detail: LOD methods balance visual quality and computational efficiency; Gaussian splatting requires joint LOD modeling from view and 3D-scene levels.
  • Concurrent Methods: Concurrent Gaussian LOD methods differ in inputs and selection strategies, including multi-resolution point clouds and distance-interval-based LOD fusion.

B. Scaffold-GS

Scaffold-GS manages Gaussian primitives through anchors that emit neural Gaussians, whose properties are decoded from anchor features before rasterization. Octree-GS extends this anchor-based representation with hierarchical LOD organization and dynamic anchor selection.

  • B. Scaffold-GS: Each Scaffold-GS anchor stores local structural features and emits k neural Gaussians for rendering.The Gaussian positions are determined from the anchor position, predicted offsets, and a scaling factor.
  • B. Scaffold-GS: Gaussian opacities, scales, rotations, and colors are decoded from anchor features through corresponding MLPs.Opacity is produced by an opacity MLP using the anchor feature, relative viewing distance, and camera direction.
  • B. Scaffold-GS: During rendering, the emitted neural Gaussians are passed into a tile-based rasterizer, while gradients guide anchor densification and low-transparency anchors are removed.New anchors are established where neural-Gaussian gradients exceed a threshold.
  • B. Scaffold-GS: Octree-GS hierarchically organizes anchors into an octree, dynamically selecting appropriate LOD levels for efficient training and rendering across complex or large-scale scenes.The framework supports anchors that emit explicit or neural Gaussian primitives.
  • B. Scaffold-GS: The Octree-GS framework comprises octree construction, LOD-aware growing and pruning, coarse-to-fine progressive training, and appearance embedding.These components address anchor organization, adaptive control, optimization, and appearance variation.

A. LOD-structured Anchors

Octree-GS places anchors in multi-resolution voxel grids and uses cumulative LOD selection to adapt rendered detail to viewing conditions. Anchor biases and transition blending supplement local complexity and smooth level changes.

  • A. LOD-structured Anchors: Anchors are placed at centers of sparse, uniform voxel grids whose voxel sizes decrease as LOD increases.LOD 0 is the coarsest level, while higher levels capture more detail.
  • A. LOD-structured Anchors: Rendering is cumulative: an anchor is selected when its LOD is no greater than the estimated view-dependent level, and selected primitives are rasterized together.Thus, rendering at LOD K includes primitives from LOD 0 through K.
  • A. LOD-structured Anchors: The number of octree layers is computed from robust maximum and minimum camera-to-SfM-point distances after discarding outliers with hyperparameter rd.The paper typically sets rd to 0.999.
  • A. LOD-structured Anchors: After determining K, Octree-GS constructs K-layer octree grids and voxelizes anchors at the corresponding voxel size.The coarsest layer uses base voxel size δ.
  • A. LOD-structured Anchors: Visible anchors are selected from the LOD hierarchy using projected Gaussian footprint approximated by observation distance, with focal scaling for varying camera intrinsics.The selection estimate is based on viewpoint-to-anchor distance.
  • A. LOD-structured Anchors: Opacity blending interpolates between adjacent LOD levels by adding nearly satisfied anchors with scaled opacity, producing continuous transitions.This is intended to eliminate visible LOD aliasing.

B. Adaptive Anchor Gaussians Control

Octree-GS controls anchors with gradient-guided growth, opacity and view-frequency pruning, and coarse-to-fine training. These mechanisms separate LOD roles, suppress artifacts and redundancy, and preserve rendering quality while improving speed.

  • B. Adaptive Anchor Gaussians Control: Gradient-guided densification grows new anchors in empty octree voxels when spawned Gaussian primitives exceed a predefined significance threshold.The strategy evaluates accumulated view-space positional gradients periodically.
  • B. Adaptive Anchor Gaussians Control: Higher-gradient Gaussians are promoted to the next LOD level, while scene-complexity gradients also increase an anchor’s LOD bias.The bias increment is ε = 0.01 in the empirical setting.
  • B. Adaptive Anchor Gaussians Control: Average opacity over training iterations is used to identify redundant and ineffective anchors for pruning.This pruning criterion follows a strategy similar to Scaffold-GS.
  • B. Adaptive Anchor Gaussians Control: View-frequency pruning removes anchors selected in too few training views, eliminating floaters while improving visual quality and reducing storage.The view-frequency threshold τv targets anchors that receive insufficient optimization because they are rarely visible.
  • C. Progressive Training: Simultaneous optimization entangles LOD roles and creates large overlaps, motivating coarse-to-fine activation of progressively finer levels.The ablated visualization contrasts this entangled distribution with the full model’s separated LOD roles.
  • C. Progressive Training: Progressive training starts from a lower-LOD subset, activates an additional level every N iterations, and allocates more iterations to coarser levels.The method empirically starts from floor(K/2) levels and uses Ni−1 = ωNi with ω ≥ 1.
  • C. Progressive Training: Coarse-to-fine training arranges anchors more faithfully across LODs, reducing redundancy and accelerating rendering without reducing rendering quality.The full-model visualization shows LOD 0 capturing rough contents while higher levels recover missed high-frequency details.

D. Appearance Embedding

The paper evaluates appearance handling across diverse scenes and exposure conditions, including large-scale datasets and a self-captured tree scene.

  • D. Appearance Embedding: Generative Latent Optimization provides each anchor with a learnable appearance code to address inconsistent exposure compensation across training images.The appearance code is used as an additional input for generating Gaussian colors.
  • D. Appearance Embedding: The evaluation covers small-scale and large-scale scenes from multiple public datasets, including Mip-NeRF360, Tanks&Temples, DeepBlending, BungeeNeRF, MatrixCity, MegaNeRF, and UrbanScene3D.The study includes 21 small-scale scenes and 7 large-scale scenes.
  • D. Appearance Embedding: The real-world comparison reports rendering quality alongside the number of Gaussian primitives rendered per view.The caption states that Octree-GS achieves superior rendering quality with fewer rendered primitives.

2) Metrics:

The evaluation measures visual quality, rendering efficiency, storage, and training conditions across standard and large-scale Gaussian representations.

  • 2) Metrics:: The study reports PSNR, SSIM, LPIPS, file size, average selected Gaussian primitives per view, and rendering speed in FPS.These metrics jointly assess visual quality, memory efficiency, and rendering efficiency.
  • 2) Metrics:: Comparisons include 2D-GS, 3D-GS, Scaffold-GS, Mip-Splatting, CityGaussian, and Hierarchical-GS across small-scale and large-scale datasets.Depth supervision is removed from Hierarchical-GS for consistency.
  • 2) Metrics:: Anchor versions of 2D-GS and 3D-GS are created by voxelizing SfM points into anchors while retaining Gaussian assignments and Scaffold-GS-style densification.These variants are called Anchor-2D-GS and Anchor-3D-GS.
  • 2) Metrics:: Training uses 40k iterations for small-scale scenes and 100k iterations for large-scale scenes, with densification ending halfway through each schedule.The reported schedules end densification at 20k and 50k iterations, respectively.
  • 2) Metrics:: All experiments run on a single NVIDIA A100 80G GPU, with images stored on the CPU to avoid image-storage effects on GPU memory.The implementation also sets distinct voxel sizes for anchor baselines and Octree-GS.

B. Results Analysis

Across indoor, outdoor, small-scale, and large-scale scenes, Octree-GS preserves fine details while reducing rendered primitives and storage requirements.

  • B. Results Analysis: Octree-GS consistently outperforms baselines in fine details and texture-less regions across small-scale and large-scale scenes.The comparison includes qualitative results in Figures 5–7 and quantitative results in Tables I–III.
  • B. Results Analysis: Octree-GS reduces the number of Gaussian primitives used for rendering, producing faster rendering and lower storage overhead.The reported explanation attributes this to LOD organization and view-frequency pruning.
  • B. Results Analysis: On BungeeNeRF, baseline Gaussian counts rise notably from scale-1 to scale-4, whereas Octree-GS maintains a significantly lower count across levels.Scale-1 denotes closest views and scale-4 covers the entire landscape.
  • B. Results Analysis: On MatrixCity, Octree-GS maintains rendering speed above 30 FPS at 2k resolution across three novel-view trajectories, while baselines do not meet real-time performance.The trajectories are summarized in Table V.
  • B. Results Analysis: The anchor design is compatible with multiple Gaussian representations and organizes modified 2D-GS and 3D-GS variants with fewer file-storage requirements.The paper presents this as a more compact organization that reduces redundancy.

2) Efficiency Analysis:

Octree-GS targets consistent real-time rendering across camera trajectories and scales while preserving detail, reducing training time, and handling resolution and initialization changes.

  • 2) Efficiency Analysis:: Octree-GS maintains fine-grained detail in close views while providing consistent rendering speeds at larger scales.The evaluation uses zoom-in, 360-degree circling, and multi-scale circling trajectories.
  • 2) Efficiency Analysis:: Rendering speed is nearly 10× faster than Scaffold-GS in large-scale scenes and extreme-view sequences.The paper attributes this acceleration to its LOD structure design.
  • 2) Efficiency Analysis:: Our-Scaffold-GS completes LOD construction and optimization in a single stage in 35 minutes, compared with 69 minutes for the first stage of the proposed method.The broader Mip-NeRF360 comparison reports 20, 21, and 23 minutes for Our-2D-GS, Our-3D-GS, and Our-Scaffold-GS, respectively.
  • 3) Robustness Analysis:: Across four BungeeNeRF scales, Octree-GS captures scene details and models the entire scene with fewer Gaussian primitives.The scales range from ground-level to satellite-level camera altitudes.
  • 3) Robustness Analysis:: Across downsampling scales 1, 2, 4, and 8, Octree-GS adapts to changing footprint sizes and avoids the erosion artifacts observed for 3D-GS and Scaffold-GS.Its results are competitive with Mip-Splatting and closer to ground truth.
  • 3) Robustness Analysis:: With random initialization on Mip-NeRF360, Our-Scaffold-GS improves LPIPS from 0.31 to 0.27, PSNR from 25.93 to 26.41, and SSIM from 0.76 to 0.77.The paper attributes the improvement primarily to its efficient densification strategy.

C. Ablation Studies

The ablations evaluate Octree-GS modules on Mip-NeRF360 and qualitatively on MatrixCity, showing that next-level growth, LOD bias, progressive training, and view-frequency pruning each contribute to quality or efficiency.

  • Experimental setup: The ablation study evaluates individual modules quantitatively on Mip-NeRF360 and qualitatively on MatrixCity Block Small, using octree-Scaffold-GS as the full model.Vanilla Scaffold-GS serves as the baseline.
  • Next-level anchor growing: Constraining new anchors to grow only at the same LOD reduces rendered primitives and storage but significantly degrades visual quality.Higher-LOD finer anchors improve high-frequency detail capture.
  • LOD bias: LOD bias improves rendering quality in high-frequency regions, restoring continuous and complete white stripes on black buildings.The comparison is shown between the full model and the variant without LOD bias.
  • Progressive training: Progressive training reduces rendered Gaussian redundancy, decouples different scales into corresponding LOD levels, and improves scene reconstruction accuracy.The full model produces more noticeable building windows than the variant without progressive training.
  • View-frequency pruning: View-frequency-aware pruning eliminates floaters, particularly in close-up views, by addressing under-optimized higher-LOD anchors.Higher-LOD anchors are rendered and optimized only when the camera is close, limiting their optimization opportunities.

VI. LIMITATIONS AND CONCLUSION

Octree-GS organizes Gaussian anchors hierarchically and dynamically selects LODs to improve detail capture while maintaining real-time rendering performance without increasing Gaussian count. The authors note remaining tuning requirements and inherited 3D-GS limitations.

  • Conclusion: Octree-GS introduces LOD into Gaussian representations through an octree that hierarchically organizes anchors.The model fetches LOD levels according to observed views and scene complexity.
  • Conclusion: Dynamic LOD selection supports consistent rendering performance and adaptive detail capture without increasing the number of Gaussian primitives.The conclusion frames this as enabling high-quality interactive 3D scene and content experiences.
  • Limitations: Octree construction and progressive training require hyperparameter tuning, including balancing anchors across LOD levels and adjusting training-iteration activation.These requirements are identified as design considerations for the model components.
  • Limitations: The method remains dependent on precise camera poses and lacks geometry support, limitations shared with 3D-GS and left for future work.The authors identify both issues as unresolved challenges.

VII. SUPPLEMENTARY MATERIAL

The supplementary material provides additional quantitative results across multiple datasets, resolutions, image-quality metrics, rendered primitive counts, and storage size.

  • Supplementary results: Supplementary results cover image-quality metrics including PSNR and LPIPS, along with rendered Gaussian primitive counts and storage memory.The stated supplementary metrics also include SSIM in the listed tables.
  • Mip-NeRF360: Additional tables report PSNR and SSIM results for all scenes in the Mip-NeRF360 dataset.The supplementary material separately lists LPIPS, primitive-count, and storage tables for Mip-NeRF360.
  • Additional datasets: Supplementary tables provide quantitative results for the Tanks&Temples and DeepBlending datasets.The tables are identified as dataset-level quantitative results without values in the supplied passages.
  • BungeeNeRF: BungeeNeRF supplementary tables report PSNR, SSIM, LPIPS, rendered Gaussian primitive counts, and storage memory across scenes.The listed tables cover each of these measures separately.
  • Multi-resolution evaluation: Additional multi-resolution quantitative results are listed for Tanks&Temples and Deep Blending.The supplied table captions do not state the individual metric values.
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