← 研踪 PaperTrailarXiv:2605.31466 · v2
VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching
首次提交 2026-05-29 23:59
版本更新 2026-10-27 09:33
雷达观测 2026-10-29 00:35
摘要
Reconstructing the complete geometry of a scene from a single RGB image remains challenging, especially when inferring hidden structures where visual evidence is incomplete. We introduce VolFill, a generative framework that predicts the 3D structure of the complete scene rather than relying on traditional pixel-aligned regression. Our method utilizes a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into a compact latent space, paired with a latent Diffusion Transformer that denoises this representation to recover the complete scene. We condition the generation on a geometry foundation model, leveraging rich spatial priors for robust reasoning. Unlike existing methods limited by per-ray constraints or unstructured point-cloud queries, VolFill provides a structured representation that supports direct surface extraction and occupancy queries at scale. Extensive experiments on the SCRREAM and NRGB-D datasets demonstrate that our approach significantly outperforms current amodal baselines, providing a robust foundation for holistic spatial understanding.
作者备注：Accepted to NeurIPS 2026. Project page: https://ngoductuanlhp.github.io/VolFill/
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v2 · 2026-10-27 09:33 · 最新观测版本
VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching
Reconstructing the complete geometry of a scene from a single RGB image remains challenging, especially when inferring hidden structures where visual evidence is incomplete. We introduce VolFill, a generative framework that predicts the 3D structure of the complete scene rather than relying on traditional pixel-aligned regression. Our method utilizes a hybrid 3D VAE to compress sparse truncated unsigned distance function grids into a compact latent space, paired with a latent Diffusion Transformer that denoises this representation to recover the complete scene. We condition the generation on a geometry foundation model, leveraging rich spatial priors for robust reasoning. Unlike existing methods limited by per-ray constraints or unstructured point-cloud queries, VolFill provides a structured representation that supports direct surface extraction and occupancy queries at scale. Extensive experiments on the SCRREAM and NRGB-D datasets demonstrate that our approach significantly outperforms current amodal baselines, providing a robust foundation for holistic spatial understanding.
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