Scene-SAM3D: Multi-View Scene Asset Generation Without Fine-Tuning
Scene-SAM3D improves multi-view 3D scene generation without fine-tuning, reducing CD by 43.8% on Replica.
Key Findings
Methodology
Scene-SAM3D is a training-free framework extending SAM3D from single-view to multi-view scene generation. It selects complementary views to reduce redundancy, performs latent velocity fusion to integrate multi-view evidence, and uses rigid-object Gaussian optimization to refine scene layout within 200 iterations.
Key Results
- On the Replica dataset, Scene-SAM3D reduces scene-level CD by 43.8% and on ScanNet++ by 30.9%, while cutting FLOPs and latency by nearly 20% under the same multi-view setting.
- At the instance level, Scene-SAM3D reduces CD by 5.36% over single-view SAM3D on Replica and improves Completeness by 6.98% on ScanNet++.
- Compared to SAM3D using MultiDiffusion fusion, Scene-SAM3D further reduces CD by 3.93%/1.97% on Replica/ScanNet++.
Significance
Scene-SAM3D significantly improves the quality and efficiency of multi-view 3D scene generation without fine-tuning. It addresses challenges of occlusion, redundancy, and cross-view inconsistencies in real-world scenes, providing more reliable 3D asset generation for applications like robotic manipulation, navigation, and simulation.
Technical Contribution
Scene-SAM3D combines selection-fusion-alignment processes, integrating occupancy-coverage view selection, early-stop multi-view latent fusion, and rigid-object Gaussian layout refinement, enhancing geometric and scene consistency without increasing computational cost.
Novelty
Scene-SAM3D is the first to achieve multi-view scene generation without fine-tuning, introducing occupancy-coverage view selection and conflict-aware multi-view fusion strategies to resolve geometric conflicts caused by view inconsistencies.
Limitations
- In heavily occluded scenes, while Scene-SAM3D improves generation quality, it may still have unresolved geometric detail omissions.
- The method may be limited in handling dynamic scenes due to its assumption of static consistency between views.
- In extremely complex scenes, computational costs may significantly increase.
Future Work
Future research directions include exploring multi-view generation methods for dynamic scenes, optimizing computational efficiency, and integrating deep learning techniques to further enhance generation quality.
AI Executive Summary
In modern computer vision, high-quality 3D scene generation is crucial for applications like robotic manipulation, navigation, and simulation. However, existing single-view 3D generation models face challenges such as occlusion, redundancy, and cross-view inconsistencies when dealing with real-world scenes. Scene-SAM3D significantly improves the quality and efficiency of multi-view scene generation through a selection-fusion-alignment process.
Scene-SAM3D operates without fine-tuning by selecting complementary views to reduce redundancy, performing latent velocity fusion to integrate multi-view evidence, and using rigid-object Gaussian optimization to refine scene layout within 200 iterations. In experiments, Scene-SAM3D reduces CD by 43.8% on Replica and 30.9% on ScanNet++, while cutting FLOPs and latency by nearly 20%.
This method is significant for both academia and industry, providing more reliable 3D asset generation solutions. Future research directions include exploring multi-view generation methods for dynamic scenes, optimizing computational efficiency, and integrating deep learning techniques to further enhance generation quality.
Deep Analysis
Background
3D scene generation holds a significant position in computer vision, especially in fields like robotic manipulation, navigation, and simulation. Traditional single-view generation models like SAM3D perform well in object generation but face challenges with complex scenes due to occlusion and view inconsistencies. The emergence of multi-view generation models partially addresses these issues, but further optimization is needed.
Core Problem
In real-world scenes, single-view generation models struggle with complex occlusion and redundancy issues, leading to inconsistent and incomplete generation results. Multi-view generation requires coordination between different views to ensure accurate geometry and scene layout.
Innovation
Scene-SAM3D innovatively addresses redundancy and conflict issues in multi-view generation through a selection-fusion-alignment process. Its occupancy-coverage view selection strategy ensures complementary views, while its conflict-aware multi-view fusion strategy resolves geometric conflicts.
Methodology
- �� Occupancy-Coverage View Selection: Selects an anchor view and multiple helper views to reduce redundancy.
- �� Conflict-Aware Multi-View Fusion: Integrates multi-view information during early sampling phases to avoid conflicts.
- �� Rigid-Object Gaussian Layout Optimization: Optimizes scene layout through multi-view feedback, maintaining geometric consistency.
Experiments
Experiments were conducted on the Replica and ScanNet++ datasets, comparing Scene-SAM3D with single-view and multi-view baseline methods. Metrics such as Chamfer Distance (CD) and Completeness were used to evaluate generation quality, and the impact of different view selection strategies was analyzed.
Results
Scene-SAM3D reduces CD by 43.8% on Replica and 30.9% on ScanNet++. Compared to existing multi-view methods, Scene-SAM3D significantly improves generation quality without increasing computational costs.
Applications
Scene-SAM3D can be used in fields like robotic manipulation, navigation, and simulation, providing higher quality 3D scene generation. Its no fine-tuning requirement makes it easy to integrate into existing systems, enhancing industrial application feasibility.
Limitations & Outlook
Despite Scene-SAM3D's excellent performance in multi-view generation, challenges remain in handling dynamic scenes and extremely complex scenes. Future research will focus on improving computational efficiency and generation quality.
Plain Language Accessible to non-experts
Imagine you're playing a large puzzle game, trying to observe from different angles to complete the puzzle. Scene-SAM3D acts like a smart assistant, choosing the best observation angles to reduce redundant observations and integrating this information early on to ensure each piece fits perfectly. Finally, it goes through a fine-tuning process to ensure the entire puzzle layout is harmonious and consistent.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super cool 3D puzzle game. You need to look at each puzzle piece from different angles to fit them together perfectly. Scene-SAM3D is like your game assistant, helping you choose the best angles, reducing redundant observations, and integrating this information early on to ensure each piece fits perfectly. Finally, it goes through a fine-tuning process to ensure the entire puzzle layout is harmonious and consistent. Isn't that awesome?
Glossary
Chamfer Distance (CD)
A metric for measuring the similarity between two sets of point clouds; lower values indicate higher similarity.
Used to evaluate the geometric accuracy of generated 3D models.
Latent Velocity Fusion
A method for integrating information from different views in multi-view generation by fusing latent velocity fields to improve generation quality.
Used to integrate multi-view evidence and reduce geometric conflicts.
Rigid-Object Gaussian Optimization
A method for optimizing scene layout using Gaussian distributions to ensure geometric consistency of generated objects.
Used to optimize the layout of generated objects in the scene.
Occupancy-Coverage View Selection
A method for selecting the best combination of views to ensure complementarity and maximize information.
Used to reduce redundant observations and improve generation quality.
Multi-View Generation
A method for generating 3D models using information from multiple views, aiming to improve completeness and consistency.
Used to address occlusion and inconsistency issues in single-view generation.
Open Questions Unanswered questions from this research
- 1 How to achieve efficient multi-view generation in dynamic scenes? Current methods assume static consistency between views, making it difficult to handle dynamic changes.
- 2 How to optimize computational costs in extremely complex scenes? Current methods may significantly increase computational costs when handling complex scenes.
- 3 How to integrate deep learning techniques to further enhance generation quality? Current methods mainly rely on traditional geometric optimization strategies.
Applications
Immediate Applications
Robotic Manipulation
Scene-SAM3D can be used to improve robot operation efficiency in complex environments by providing more accurate 3D scene information.
Virtual Reality
In virtual reality applications, Scene-SAM3D can be used to generate more realistic 3D scenes, enhancing user experience.
Long-term Vision
Autonomous Driving
Scene-SAM3D can be used in autonomous driving systems for environmental perception, providing more accurate 3D scene information and improving safety.
Abstract
High-quality 3D scene assets are critical for embodied applications such as robotic manipulation, navigation, and simulation. Despite their strong object priors, recent single-image 3D generation models such as SAM3D remain insufficient for real-world scenes, where severe occlusions, redundant observations, and cross-view inconsistencies make reliable scene generation challenging. We introduce Scene-SAM3D, a training-free framework that extends SAM3D from single-view object generation to calibrated multi-view scene asset generation. Scene-SAM3D selects a compact set of complementary views, reducing observation redundancy while providing additional evidence for regions occluded in individual views. Based on the selected views, it performs step-efficient latent velocity fusion to integrate multi-view evidence and suppress cross-view conflicts in canonical space. Finally, a lightweight rigid-object Gaussian optimization refines the scene layout within 200 iterations while preserving the generated object geometry. Experiments on Replica and ScanNet++ demonstrate consistent improvements at both instance and scene levels, with our method reducing scene-level CD by 43.8% on Replica and 30.9% on ScanNet++, while cutting flow-model sampling FLOPs and wall-time latency by nearly 20% under the same multi-view setting. Code will be released at https://github.com/xibi777/Scene-SAM3D.