Neural 3D scene representations have shown great potential for 3D
reconstruction from 2D images. However, reconstructing real-world captures of
complex scenes still remains a challenge. Existing generic 3D reconstruction
methods often struggle to represent fine geometric details and do not
adequately model reflective surfaces of large-scale scenes. Techniques that
explicitly focus on reflective surfaces can model complex and detailed
reflections by exploiting better reflection parameterizations. However, we
observe that these methods are often not robust in real unbounded scenarios
where non-reflective as well as reflective components are present. In this
work, we propose UniSDF, a general purpose 3D reconstruction method that can
reconstruct large complex scenes with reflections. We investigate both
view-based as well as reflection-based color prediction parameterization
techniques and find that explicitly blending these representations in 3D space
enables reconstruction of surfaces that are more geometrically accurate,
especially for reflective surfaces. We further combine this representation with
a multi-resolution grid backbone that is trained in a coarse-to-fine manner,
enabling faster reconstructions than prior methods. Extensive experiments on
object-level datasets DTU, Shiny Blender as well as unbounded datasets Mip-NeRF
360 and Ref-NeRF real demonstrate that our method is able to robustly
reconstruct complex large-scale scenes with fine details and reflective
surfaces. Please see our project page at
this https URL

提出了 UniSDF，一个可以重建具有反射表面的大规模复杂场景的通用 3D 重建方法。通过在 3D 空间中明确混合视图和反射颜色预测参数化技术，使得重建的表面在几何上更准确，特别是对于反射表面。通过与多分辨率网格主干结合，该方法以粗糙到精细的方式进行训练，实现比以前的方法更快的重建速度。大量实验表明，该方法能够稳健地重建具有精细细节和反射表面的复杂大规模场景。

UniSDF：统一神经表示用于具有反射的复杂场景的高保真度 3D 重建

UniSDF: Unifying Neural Representations for High-Fidelity 3D  Reconstruction of Complex Scenes with Reflections

We study the problem of inferring an object-centric scene representation from
a single image, aiming to derive a representation that explains the image
formation process, captures the scene's 3D nature, and is learned without
supervision. Most existing methods on scene decomposition lack one or more of
these characteristics, due to the fundamental challenge in integrating the
complex 3D-to-2D image formation process into powerful inference schemes like
deep networks. In this paper, we propose unsupervised discovery of Object
Radiance Fields (uORF), integrating recent progresses in neural 3D scene
representations and rendering with deep inference networks for unsupervised 3D
scene decomposition. Trained on multi-view RGB images without annotations, uORF
learns to decompose complex scenes with diverse, textured background from a
single image. We show that uORF enables novel tasks, such as scene segmentation
and editing in 3D, and it performs well on these tasks and on novel view
synthesis on three datasets.

本文提出了一种无监督的方法，叫做 uORF，用于从单张图像中推断物体场景表示，该方法整合了神经 3D 场景表示和深度推断网络，能够成功地分解复杂的 3D 场景，并完成诸如场景分割、3D 编辑和新视角合成等任务。