Rein3D: Reinforced 3D Indoor Scene Generation with Panoramic Video Diffusion Models

2026-06-08 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

Rein3D is a novel framework designed for generating high-quality 3D indoor scenes from sparse inputs, addressing challenges in inferring massive missing geometry and maintaining global consistency. It couples explicit 3D Gaussian Splatting (3DGS) with temporally coherent priors from panoramic video diffusion models. The approach follows a "restore-and-refine" paradigm, using a radial exploration strategy to render imperfect panoramic videos from a coarse 3DGS initialization. These sequences are then restored by a panoramic video-to-video diffusion model, enhanced via video super-resolution, and used as pseudo-ground truths to update the global 3D Gaussian field. To support this, the PanoV2V-15K dataset was introduced, comprising over 15,000 paired clean and degraded panoramic videos. Experiments show Rein3D produces photorealistic, globally consistent 3D scenes and significantly improves long-range camera exploration compared to baselines. The video restoration network is fine-tuned on PanoV2V-15K for 5,000 steps on 4 NVIDIA H200 GPUs, with 3DGS optimized for 15,000 steps.

Key takeaway

For AI Scientists or Machine Learning Engineers developing Embodied AI or VR applications, Rein3D offers a robust solution for high-fidelity 3D indoor scene generation. You should consider its "restore-and-refine" paradigm, which leverages panoramic video diffusion and 3D Gaussian Splatting to overcome occlusion and geometric inconsistency. This approach enables photorealistic, globally consistent scenes, significantly improving long-range camera exploration. Evaluate integrating its spherical adaptation techniques and the PanoV2V-15K dataset into your scene reconstruction pipelines for superior results.

Key insights

Rein3D integrates 3D Gaussian Splatting with panoramic video diffusion to restore and refine 3D indoor scenes from sparse inputs.

Principles

• Couple explicit 3DGS with video diffusion priors.
• Use radial exploration for occluded region discovery.
• Adapt noise and loss for spherical geometry.

Method

Rein3D initializes coarse 3DGS, renders imperfect panoramic videos via radial exploration, restores them with a panoramic video-to-video diffusion model, enhances via super-resolution, and refines the global 3DGS.

In practice

• Utilize PanoV2V-15K for panoramic video restoration.
• Apply Latitude-Aware Sampling for spherical noise.
• Implement Latitude-Decay Loss for polar pixel weighting.

Topics

• 3D Scene Generation
• 3D Gaussian Splatting
• Video Diffusion Models
• Panoramic Video
• Embodied AI
• PanoV2V-15K Dataset
• Virtual Reality

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.