Abstract Rendering: Computing All that is Seen in Gaussian Splat Scenes

• URL: http://arxiv.org/abs/2503.00308v2
• Date: Tue, 04 Mar 2025 06:08:00 GMT
• Title: Abstract Rendering: Computing All that is Seen in Gaussian Splat Scenes
• Abstract summary: We introduce abstract rendering, a method for computing a set of images by rendering a scene from a continuously varying range of camera positions.<n>This capability is particularly valuable for the formal verification of vision-based autonomous systems and other safety-critical applications.
• Score: 9.906888027611576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce abstract rendering, a method for computing a set of images by rendering a scene from a continuously varying range of camera positions. The resulting abstract image-which encodes an infinite collection of possible renderings-is represented using constraints on the image matrix, enabling rigorous uncertainty propagation through the rendering process. This capability is particularly valuable for the formal verification of vision-based autonomous systems and other safety-critical applications. Our approach operates on Gaussian splat scenes, an emerging representation in computer vision and robotics. We leverage efficient piecewise linear bound propagation to abstract fundamental rendering operations, while addressing key challenges that arise in matrix inversion and depth sorting-two operations not directly amenable to standard approximations. To handle these, we develop novel linear relational abstractions that maintain precision while ensuring computational efficiency. These abstractions not only power our abstract rendering algorithm but also provide broadly applicable tools for other rendering problems. Our implementation, AbstractSplat, is optimized for scalability, handling up to 750k Gaussians while allowing users to balance memory and runtime through tile and batch-based computation. Compared to the only existing abstract image method for mesh-based scenes, AbstractSplat achieves 2-14x speedups while preserving precision. Our results demonstrate that continuous camera motion, rotations, and scene variations can be rigorously analyzed at scale, making abstract rendering a powerful tool for uncertainty-aware vision applications.

Related papers

• IRIS: Intersection-aware Ray-based Implicit Editable Scenes [1.9659643719859716]
  IRIS (Intersection-aware Ray-based Implicit Editable Scenes) is introduced as a method for efficient and interactive scene editing.<n>To overcome the limitations of standard ray marching, an analytical sampling strategy is employed that precisely identifies interaction points between rays and scene primitives.<n>By interpolating latent attributes from sorted intersections, costly 3D searches are bypassed, ensuring consistency, enabling high-fidelity, real-time rendering, and flexible shape editing.
  arXiv  Detail & Related papers  (2026-03-16T14:44:32Z)
• Virtual Memory for 3D Gaussian Splatting [1.278093617645299]
  Gaussian Splatting represents a breakthrough in the field of novel view rendering.<n>Recent advances have increased the size of Splatting scenes that can be created.
  arXiv  Detail & Related papers  (2025-06-24T08:31:33Z)
• PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification [6.3286311412189304]
  We propose an efficient approach that leverages polygonal representations of images using dominant points or contour coordinates. Our method significantly reduces computational requirements, accelerates training, and conserves resources. Experiments on benchmark datasets validate the effectiveness of our approach in reducing complexity, improving generalization, and facilitating edge computing applications.
  arXiv  Detail & Related papers  (2025-04-01T22:05:00Z)
• SparseGS-W: Sparse-View 3D Gaussian Splatting in the Wild with Generative Priors [22.561786156613525]
  We propose SparseGS-W, a novel framework to Synthesizing novel views of large-scale scenes from unconstrained in-the-wild images. We leverage geometric priors and constrained diffusion priors to compensate for the lack of multi-view information from extremely sparse input. SparseGS-W achieves state-of-the-art performance not only in full-reference metrics, but also in commonly used non-reference metrics such as FID, ClipIQA, and MUSIQ.
  arXiv  Detail & Related papers  (2025-03-25T08:40:40Z)
• Image-GS: Content-Adaptive Image Representation via 2D Gaussians [55.15950594752051]
  We propose Image-GS, a content-adaptive image representation. Using anisotropic 2D Gaussians as the basis, Image-GS shows high memory efficiency, supports fast random access, and offers a natural level of detail stack. General efficiency and fidelity of Image-GS are validated against several recent neural image representations and industry-standard texture compressors. We hope this research offers insights for developing new applications that require adaptive quality and resource control, such as machine perception, asset streaming, and content generation.
  arXiv  Detail & Related papers  (2024-07-02T00:45:21Z)
• Rasterized Edge Gradients: Handling Discontinuities Differentiably [25.85191317712521]
  We present a novel method for computing gradients at discontinuities for rendering approximations. Our method elegantly simplifies the traditionally complex problem through a carefully designed approximation strategy. We showcase our method in human head scene reconstruction, demonstrating handling of camera images and segmentation masks.
  arXiv  Detail & Related papers  (2024-05-03T22:42:00Z)
• Learning Robust Multi-Scale Representation for Neural Radiance Fields from Unposed Images [65.41966114373373]
  We present an improved solution to the neural image-based rendering problem in computer vision. The proposed approach could synthesize a realistic image of the scene from a novel viewpoint at test time.
  arXiv  Detail & Related papers  (2023-11-08T08:18:23Z)
• Pixel-Inconsistency Modeling for Image Manipulation Localization [59.968362815126326]
  Digital image forensics plays a crucial role in image authentication and manipulation localization. This paper presents a generalized and robust manipulation localization model through the analysis of pixel inconsistency artifacts. Experiments show that our method successfully extracts inherent pixel-inconsistency forgery fingerprints.
  arXiv  Detail & Related papers  (2023-09-30T02:54:51Z)
• DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation [56.514462874501675]
  We propose a dynamic sparse attention based Transformer model to achieve fine-level matching with favorable efficiency. The heart of our approach is a novel dynamic-attention unit, dedicated to covering the variation on the optimal number of tokens one position should focus on. Experiments on three applications, pose-guided person image generation, edge-based face synthesis, and undistorted image style transfer, demonstrate that DynaST achieves superior performance in local details.
  arXiv  Detail & Related papers  (2022-07-13T11:12:03Z)
• A Novel Algorithm for Exact Concave Hull Extraction [0.0]
  Region extraction is necessary in a wide range of applications, from object detection in autonomous driving to analysis of subcellular morphology in cell biology. There exist two main approaches: convex hull extraction, for which exact and efficient algorithms exist and concave hulls, which are better at capturing real-world shapes but do not have a single solution. In this study, we present a novel algorithm that can provide concave hulls with maximal (i.e. pixel-perfect) resolution and is tunable for speed-efficiency tradeoffs.
  arXiv  Detail & Related papers  (2022-06-23T05:26:48Z)
• RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation [110.4255414234771]
  Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries domain randomization and differentiable rendering gradients to address this problem. Our approach achieves significantly lower reconstruction errors and has better generalizability among unknown rendering configurations.
  arXiv  Detail & Related papers  (2022-05-11T17:59:51Z)
• BoundarySqueeze: Image Segmentation as Boundary Squeezing [104.43159799559464]
  We propose a novel method for fine-grained high-quality image segmentation of both objects and scenes. Inspired by dilation and erosion from morphological image processing techniques, we treat the pixel level segmentation problems as squeezing object boundary. Our method yields large gains on COCO, Cityscapes, for both instance and semantic segmentation and outperforms previous state-of-the-art PointRend in both accuracy and speed under the same setting.
  arXiv  Detail & Related papers  (2021-05-25T04:58:51Z)

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.