DecomVoxel: Harnessing 3D-Native Priors with Guided In-situ Denoising Optimization for Decompositional Scene Reconstruction

arXiv:2610.01914 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Code: https://github.com/DecomVoxel/DecomVoxel

Terminology

• Fidelity — 30× in this paper · explained in 2 episodes
• Computer Vision and Pattern Recognition — 16× in this paper
• Consistency — 14× in this paper · explained in 3 episodes
• Distillation — 14× in this paper · explained in 2 episodes
• Alignment — 12× in this paper · explained in 1 episode
• Pruning — 9× in this paper · explained in 4 episodes
• Diffusion Model — 8× in this paper · explained in 8 episodes
• Flow Matching — 7× in this paper · explained in 8 episodes
• Gaussian Splatting — 7× in this paper · explained in 4 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Generative Model — 6× in this paper · explained in 2 episodes
• Hallucination — 5× in this paper · explained in 3 episodes
• Accuracy — 4× in this paper · explained in 1 episode
• Adam — 4× in this paper · explained in 2 episodes
• 3D Gaussian Splatting — 3× in this paper · explained in 2 episodes
• Generative Models — 3× in this paper · explained in 2 episodes
• In European Conference on Computer Vision — 3× in this paper
• Latent Representation — 3× in this paper · explained in 2 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Point Clouds — 3× in this paper · explained in 2 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• ACM Transactions on Graphics — 2× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Chamfer Distance — 2× in this paper
• Embodied AI — 2× in this paper · explained in 2 episodes
• Flow Matching Model — 2× in this paper · explained in 2 episodes
• In International Conference on Computer Vision — 2× in this paper
• Regularization — 2× in this paper · explained in 3 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptive Scheduler — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Elias — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Score Distillation Sampling — 1× in this paper
• System Identification — 1× in this paper · explained in 3 episodes
• Two-Stage Optimization — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• ReconViaGen: Towards Accurate Multi-view 3D Object Reconstruction via Generation
• SAM 3D: 3Dfy Anything in Images
• Advancing Structured Priors for Sparse-Voxel Surface Reconstruction
• LucidDreamer: Domain-free Generation of 3D Gaussian Splatting Scenes
• ReplicateAnyScene: Zero-Shot Video-to-3D Composition via Textual-Visual-Spatial Alignment
• LRM: Large Reconstruction Model for Single Image to 3D
• UniRecGen: Unifying Multi-View 3D Reconstruction and Generation
• Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details
• MV-SAM3D: Adaptive Multi-View Fusion for Layout-Aware 3D Generation
• GeoSVR: Taming Sparse Voxels for Geometrically Accurate Surface Reconstruction
• VoxHammer: Training-Free Precise and Coherent 3D Editing in Native 3D Space
• PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
• Depth Anything 3: Recovering the Visual Space from Any Views
• Flow Matching for Generative Modeling
• SceneGen: Single-Image 3D Scene Generation in One Feedforward Pass
• SVRecon: Sparse Voxel Rasterization for Surface Reconstruction
• DINOv2: Learning Robust Visual Features without Supervision
• Seedream 4.0: Toward Next-generation Multimodal Image Generation
• MVDream: Multi-view Diffusion for 3D Generation
• ShapeR: Robust Conditional 3D Shape Generation from Casual Captures

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models