OpenNovelty.org

ComGS: Efficient 3D Object-Scene Composition via Surface Octahedral Probes

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Object-Scene CompositionGaussian SplattingSurface Octahedral Probes
Abstract:

Gaussian Splatting (GS) enables immersive rendering, but realistic 3D object–scene composition remains challenging. Baked appearance and shadow information in GS radiance fields cause inconsistencies when combining objects and scenes. Addressing this requires relightable object reconstruction and scene lighting estimation. For relightable object reconstruction, existing Gaussian-based inverse rendering methods often rely on ray tracing, leading to low efficiency. We introduce Surface Octahedral Probes (SOPs), which store lighting and occlusion information and allow efficient 3D querying via interpolation, avoiding expensive ray tracing. SOPs provide at least a 2x speedup in reconstruction and enable real-time shadow computation in Gaussian scenes. For lighting estimation, existing Gaussian-based inverse rendering methods struggle to model intricate light transport and often fail in complex scenes, while learning-based methods predict lighting from a single image and are viewpoint-sensitive. We observe that 3D object–scene composition primarily concerns the object’s appearance and nearby shadows. Thus, we simplify the challenging task of full scene lighting estimation by focusing on the environment lighting at the object’s placement. Specifically, we capture a 360° reconstructed radiance field of the scene at the location and fine-tune a diffusion model to complete the lighting. Building on these advances, we propose ComGS, a novel 3D object–scene composition framework. Our method achieves high-quality, real-time rendering at around 26 FPS, produces visually harmonious results with vivid shadows, and requires only 36 seconds for editing. The code and dataset will be publicly released.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces Surface Octahedral Probes (SOPs) for efficient relightable object reconstruction and reformulates scene lighting estimation as environment map completion, culminating in the ComGS framework for realistic 3D object-scene composition. It resides in the Relightable Neural Radiance Fields leaf, which contains eight papers including sibling works like Relightable 3D Gaussians and IllumiNeRF. This leaf represents a moderately active research direction within the broader Neural Radiance Field Approaches branch, focusing on Gaussian splatting methods that enable relighting through BRDF decomposition or lighting-aware representations.

The taxonomy reveals neighboring leaves addressing related challenges: Object-Compositional Neural Fields explores object-level disentanglement without explicit relighting focus, while Inverse Rendering and Intrinsic Decomposition methods decompose scenes into materials and lighting using differentiable rendering. The scope note for Relightable Neural Radiance Fields explicitly excludes methods without relighting capabilities, positioning this work at the intersection of compositional editing and physically plausible illumination. Nearby branches like Diffusion-Based Object Insertion tackle similar composition goals through generative priors rather than explicit inverse rendering, highlighting distinct methodological philosophies within the field.

Among thirty candidates examined across three contributions, none were flagged as clearly refuting the proposed methods. For the SOPs contribution, ten candidates were reviewed with zero refutable overlaps; similarly, the environment map completion reformulation and ComGS framework each examined ten candidates without identifying substantial prior work. This suggests that within the limited search scope, the specific combination of octahedral probe-based lighting storage, environment map completion for scene lighting, and their integration into a Gaussian splatting composition pipeline appears relatively unexplored. However, the analysis covers top-K semantic matches and does not constitute exhaustive coverage of all relightable rendering literature.

Based on the limited literature search of thirty candidates, the work appears to occupy a distinct position within the relightable Gaussian splatting space, particularly in its probe-based efficiency approach and compositional focus. The absence of refutable candidates across all contributions may reflect genuine novelty in the specific technical choices or indicate that the search scope did not capture all relevant prior work in adjacent areas like traditional probe-based rendering or environment map estimation. The taxonomy context suggests this is an active but not overcrowded research direction with clear boundaries from neighboring methods.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: 3D object-scene composition with relighting and shadow rendering. This field addresses the challenge of inserting virtual objects into real scenes while ensuring physically plausible lighting interactions, including accurate shadows, reflections, and illumination consistency. The taxonomy reveals several complementary research directions. Inverse Rendering and Intrinsic Decomposition methods focus on decomposing scenes into material properties and lighting to enable downstream editing, as seen in works like Inverse Rendering Indoor[6] and Diffusion Inverse Rendering[11]. Neural Radiance Field Approaches leverage volumetric representations to model relightable scenes, with branches exploring both relightable NeRFs and Gaussian-based methods such as Relightable 3D Gaussians[3] and Relightable Gaussian Realtime[4]. Diffusion-Based Object Insertion exploits generative models for harmonizing inserted objects, exemplified by PS-Diffusion[12] and Illumidiff[26]. Physics-Based Rendering and Composition emphasizes traditional graphics pipelines and physically accurate light transport, while Specialized Applications target domains like virtual staging and automotive visualization. Foundational Techniques provide core rendering algorithms and datasets that underpin these approaches. Recent work has intensified around real-time relightable representations and generative harmonization. Neural radiance field methods trade off between rendering quality and computational efficiency, with Gaussian splatting variants like Relightable Gaussian Realtime[4] pushing toward interactive rates. ComGS[0] sits within the Relightable Neural Radiance Fields cluster, closely aligned with Relightable 3D Gaussians[3] and IllumiNeRF[9], emphasizing efficient Gaussian-based scene decomposition for object insertion with dynamic relighting. Compared to Neural Gaffer[28], which focuses on interactive lighting control, or ROGR[50], which targets robust geometry reconstruction, ComGS[0] prioritizes compositional flexibility and shadow consistency. A central tension across branches involves balancing physical accuracy against computational cost and generalization to diverse lighting conditions, with diffusion-based methods offering strong priors but less precise physical control than inverse rendering pipelines.

Claimed Contributions

Surface Octahedral Probes (SOPs) for efficient relightable object reconstruction

The authors propose Surface Octahedral Probes (SOPs), a novel data structure that stores indirect lighting and occlusion information near object surfaces. SOPs enable efficient querying through interpolation rather than costly ray tracing, achieving at least a 2× speedup in reconstruction while maintaining comparable accuracy to state-of-the-art methods.

10 retrieved papers
Reformulation of scene lighting estimation as environment map completion

The authors reformulate the difficult problem of estimating lighting in complex scenes as a more tractable environment map inpainting task. They capture a 360-degree reconstructed radiance field at the object placement location and use a fine-tuned diffusion model to complete the lighting, avoiding the need for full scene lighting decomposition.

10 retrieved papers
ComGS framework for realistic 3D object-scene composition

The authors present ComGS, a complete framework for realistic 3D object-scene composition that integrates their proposed SOPs and lighting estimation approach. The framework operates in three stages (reconstruction, editing, rendering) and achieves high-quality, real-time rendering at approximately 26 FPS with visually harmonious results and realistic shadows.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Surface Octahedral Probes (SOPs) for efficient relightable object reconstruction

The authors propose Surface Octahedral Probes (SOPs), a novel data structure that stores indirect lighting and occlusion information near object surfaces. SOPs enable efficient querying through interpolation rather than costly ray tracing, achieving at least a 2× speedup in reconstruction while maintaining comparable accuracy to state-of-the-art methods.

Contribution

Reformulation of scene lighting estimation as environment map completion

The authors reformulate the difficult problem of estimating lighting in complex scenes as a more tractable environment map inpainting task. They capture a 360-degree reconstructed radiance field at the object placement location and use a fine-tuned diffusion model to complete the lighting, avoiding the need for full scene lighting decomposition.

Contribution

ComGS framework for realistic 3D object-scene composition

The authors present ComGS, a complete framework for realistic 3D object-scene composition that integrates their proposed SOPs and lighting estimation approach. The framework operates in three stages (reconstruction, editing, rendering) and achieves high-quality, real-time rendering at approximately 26 FPS with visually harmonious results and realistic shadows.

Table of Contents