IRIS: Intersection-aware Ray-based Implicit Editable Scenes

Grzegorz Wilczyński; Mikołaj Zieliński; Krzysztof Byrski; Joanna Waczyńska; Dominik Belter; Przemysław Spurek

cs.CVMar 16, 2026

IRIS: Intersection-aware Ray-based Implicit Editable Scenes

Authors: Grzegorz Wilczyński, Mikołaj Zieliński, Krzysztof Byrski, Joanna Waczyńska, Dominik Belter, Przemysław Spurek

Organizations: Jagiellonian University, Faculty of Mathematics and Computer Science · IDEAS Research Institute · Poznań University of Technology, Institute of Robotics and Machine Intelligence

Abstract

Neural Radiance Fields achieve high-fidelity scene representation but suffer from costly training and rendering, while 3D Gaussian splatting offers real-time performance with strong empirical results. Recently, solutions that harness the best of both worlds by using Gaussians as proxies to guide neural field evaluations, still suffer from significant computational inefficiencies. They typically rely on stochastic volumetric sampling to aggregate features, which severely limits rendering performance. To address this issue, a novel framework named IRIS (Intersection-aware Ray-based Implicit Editable Scenes) is introduced as a method designed for efficient and interactive scene editing. To overcome the limitations of standard ray marching, an analytical sampling strategy is employed that precisely identifies interaction points between rays and scene primitives, effectively eliminating empty space processing. Furthermore, to address the computational bottleneck of spatial neighbor lookups, a continuous feature aggregation mechanism is introduced that operates directly along the ray. By interpolating latent attributes from sorted intersections, costly 3D searches are bypassed, ensuring geometric consistency, enabling high-fidelity, real-time rendering, and flexible shape editing. Code can be found at https://github.com/gwilczynski95/iris.

Figures & tables

Figure 1: The framework introduces the Ray Intersection Selector (RIS), which eliminates redundant sampling to achieve high-throughput inference compared to volumetric baselines. This explicit anchor-based representation facilitates seamless integration with physics engines and robust part-level scene editing. Consequently, high-fidelity reconstruction is maintained.

Figure 2: Overview of the IRIS pipeline. The rendering process begins with the Ray Intersection Selector (RIS) , which analytically determines the precise intersection points between camera rays and 3D Gaussians, thereby significantly reducing the number of required samples. In the subsequent Ray-Coherent Aggregation (RCA) stage, local neural features are aggregated from these intersected primitives utilizing Mahalanobis distance-based weighting to synthesize a continuous latent vector. Shallow MLPs decode this representation into view-dependent color and material density, $\sigma$ . Finally, the neural density is spatially modulated by the explicit Gaussian opacity and integrated via point-based volumetric rendering to produce the final image.

Panel of Figure 3 — Figure 3: Qualitative comparison on indoor scenes from the Mip-NeRF 360 dataset. Reconstruction fidelity is evaluated against editable baselines: Radiance Meshes and EKS. It is observed that IRIS consistently achieves superior visual quality, effectively recovering high-frequency details that are blurred or lost in competing methods.

Figure 4: Sampling strategy comparison. The proposed Ray Intersection Selector (RIS) (left) analytically determines precise interaction points with scene primitives, generating meaningful samples in a single pass without processing empty space. In contrast, standard volumetric sampling (right) relies on a heuristic, multi-stage process (coarse and fine) that inherently allocates the computational budget to unoccupied regions.

Panel of Figure 5 — Figure 5: Qualitative comparison against static state-of-the-art baselines on complex real-world scenes. TS denotes Triangle Splatting [ 9 ] . It is observed that IRIS achieves reconstruction fidelity highly competitive with leading static representations such as 3DGS or Mip-NeRF 360. Despite the additional architectural constraints required for editability, fine geometric details-such as the thin structures in the Bicycle scene and the surface textures of the Truck -are preserved with precision.

Figure 6: Qualitative comparison on the NeuralEditor deformation benchmark. The rendered views demonstrate the capability of IRIS to maintain high visual fidelity under significant geometric deformation. It is observed that while baselines such as GaMeS and NeuralEditor exhibit artifacts of texture degradation, IRIS produces crisp, photorealistic renderings with preserved view-dependent effects (e.g., specular highlights on the cymbals).

	Deep Blending		Tanks&Temples
	drjohnson	playroom	train	truck	Avg.
Static
INGP	28.26	21.67	20.46	23.38	23.44
MNeRF360	29.14	29.66	19.52	24.91	25.81
3DGS	28.77	30.04	21.10	25.19	26.27
TS	28.91	29.35	21.33	24.94	26.13

Table 1: Quantitative analysis. TS: Triangle Splatting, RM: Radiance Meshes ( ⋆ re-evaluated). IRIS achieves state-of-the-art fidelity on Deep Blending and competitive performance on Tanks&Temples, confirming its overall robustness against all baselines.

Static
Model	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	Training time $\downarrow$	FPS $\uparrow$
InstantNGP	29.31	0.939	0.053	7min 27s	1.894
Nerfacto	29.19	0.941	0.095	9m 38s	0.860
Editable
NeuralEditor	31.41	0.955	0.036	103h 45min	0.083
EKS	33.12	0.962	0.031	26h 2min	0.183

Table 2: Averaged quantitative comparisons on the NeRF Synthetic dataset. While reconstruction fidelity remains comparable to the state-of-the-art editable baseline (EKS), a decisive advantage is demonstrated in efficiency, with significantly reduced training times and rendering speeds approximately 30

\times

higher compared to the EKS method.

Panel of Figure 9 — Figure 9: Qualitative comparison on the challenging HuSc3D [ 22 ] dataset. Compared to editable baselines, IRIS effectively suppresses floating artifacts prevalent in Radiance Meshes (RM) and preserves fine textures and sharp geometry that EKS over-smooths.

Panel of Figure 10 — Figure 10: Qualitative comparison on manual (top) and physics-based (bottom) editing. It is observed that EKS exhibits visible ghosting artifacts, whereas IRIS maintains superior fidelity and clean geometry, effectively eliminating trailing noise during motion.

	Outdoor scenes						Indoor scenes
	bicycle	flowers	garden	stump	treehill	Avg.	room	counter	kitchen	bonsai	Avg.
Static
INGP	22.17	20.65	25.07	23.47	22.37	22.75	29.69	26.69	29.48	30.69	29.14
Mip-NeRF 360	24.37	21.73	26.98	26.40	22.87	24.47	31.63	29.55	32.23	33.46	31.72
3DGS	25.25	21.52	27.41	26.55	22.49	24.64	30.63	28.70	30.32	31.98	30.41
Triangle Splatting	24.90	20.85	27.20	26.29	21.94	24.27	31.05	28.90	31.32	31.95	30.80

Table 3: Quantitative comparisons of reconstruction capability (PSNR) on the Mip-NeRF 360 dataset. It is observed that in indoor environments, IRIS outperforms all editable baselines and explicit static methods, being surpassed only by the implicit Mip-NeRF 360. In outdoor scenarios, while a performance gap is observed relative to Radiance Meshes, competitive fidelity is maintained against other baselines, with results significantly exceeding those of EKS. Radiance Meshes results ( ⋆ ) were re-evaluated using the official implementation.

Panel of Figure 11 — Figure 11: Editing on large-scale scenes. Non-rigid warping (top) and object scaling (bottom) are demonstrated on Bicycle and Garden scenes. It is observed that high fidelity and geometric consistency are preserved in these challenging unbounded environments.

Panel of Figure 12 — Figure 12: While standard NeRFs clip objects exiting the training volume, IRIS maintains full rendering integrity by dynamically sampling explicit geometry independent of static global bounds.

Panel of Figure 13 — Figure 13: Qualitative ablation on Hotdog and Lego . It is observed that restricted sampling (16 IPR) results in significant geometric noise. While increasing the budget (64 IPR) or removing hash initialization with full budget recovers structure, the full model is required to effectively resolve high-frequency details and specular highlights.

Appendix figures & tables7 assets

Supplementary material from the paper’s appendix.

Appendix

	Chair	Drums	Lego	Mic	Mater.	Ship	Hotdog	Ficus	Avg.
PSNR
16 IPR	32.39	24.81	30.69	32.87	27.18	28.35	34.60	28.05	29.87
64 IPR	34.94	25.61	35.43	35.16	29.40	30.80	36.95	31.45	32.47
w/o hash	34.70	25.91	34.87	35.79	29.35	30.58	36.68	32.47	32.54
full	35.00	25.51	35.54	35.87	29.46	30.69	37.02	32.12	32.65

Appendix

Table 4: Ablation study conducted on the static NeRF Synthetic dataset. The impact of the sampling budget is evaluated by varying the maximum number of intersections processed per ray (denoted as IPR), comparing restricted budgets of 16 and 64 against the full model’s 128. Additionally, the role of feature initialization is assessed by substituting the Hash Grid strategy with random initialization ”w/o hash”). It is observed that increasing the intersection quota significantly improves reconstruction fidelity, validating the need of sufficient depth coverage, while the Hash Grid initialization further contributes to the optimal performance of the full model.

	Chair	Drums	Lego	Mic	Mater.	Ship	Hotdog	Ficus	Avg.
PSNR
Naive Plotting	24.58	21.54	25.38	27.56	21.59	22.21	26.72	24.62	24.27
Neuraleditor	25.29	21.93	27.14	27.49	23.04	24.12	27.14	24.83	25.12
GaMeS	24.51	22.02	26.65	27.07	21.73	22.19	27.26	26.65	24.76
EKS	26.03	22.08	28.04	27.85	23.14	24.43	28.23	27.58	25.92
IRIS (our)	25.97	21.72	28.16	27.46	22.89	24.16	27.93	27.34	25.70

Appendix

Table 5: Quantitative comparisons on the NeuralEditor benchmark. The evaluation is conducted on the NeRF Synthetic dataset, where ground-truth deformations are applied to the objects for rigorous comparison. It is observed that IRIS achieves reconstruction quality comparable to the state-of-the-art editable method EKS, even surpassing it on specific scenes.

Panel of Figure 14 — Figure 14: Extended qualitative comparison (close-ups). This figure extends Fig. 5 from the main paper by providing additional crop views from scenes on the Mip-NeRF 360 and Deep Blending datasets. TS denotes Triangle Splatting. It is observed that IRIS preserves high-frequency structural details, achieving quality competitive with static state-of-the-art baselines like Mip-NeRF 360.

Panel of Figure 15 — Figure 15: Extended qualitative comparison (Full Views). Zoomed-out visualizations from Mip-NeRF 360, Deep Blending and Tanks&Temples datasets are presented. These views demonstrate the global consistency of the reconstruction, confirming that IRIS maintains photometric accuracy across large spatial extends.

Panel of Figure 16 — Figure 16: Additional editing results on the Mip-NeRF 360 dataset. The capability of IRIS to perform complex, topology-agnostic manipulations in large-scale unbounded environments is demonstrated. Non-rigid deformations, such as the sinusoidal warping of the bench in the Bicycle scene (top) and the rippling effect applied to the table in the Garden scene (bottom), are executed while preserving high-frequency textural details. In all cases, geometric consistency and high fidelity are successfully maintained throughout the animation sequence.

Panel of Figure 17 — Figure 17: Physics-based simulations on IRIS representations. The explicit point clouds optimized by our method were exported to Blender to drive dynamic deformations. The Ficus sequence (top) demonstrates a gravity simulation where leaves detach and fall realistically. The Lego sequence (bottom) showcases soft-body dynamics; the truck is dropped onto a platform, exhibiting elastic deformation and bouncing behavior upon collision. These examples highlight the capability of IRIS to support complex physical interactions while maintaining rendering fidelity.

Panel of Figure 18 — Figure 18: Qualitative results on the NeuralEditor benchmark. Comprehensive comparison across all scenes from the deformation dataset. NE denotes NeuralEditor. It is observed that IRIS maintains high visual fidelity and geometric consistency, avoiding the blurring artifacts seen in Naive and NeuralEditor baselines, while achieving quality comparable to the state-of-the-art EKS method.

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