IRIS: Intersection-aware Ray-based Implicit Editable Scenes

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 (18)

• 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.
• 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.
• Figure 5: Qualitative comparison against static state-of-the-art baselines on complex real-world scenes. TS denotes Triangle Splatting triangle_splatting. 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.