Neural scene reconstruction methods have achieved impressive performance in
reconstructing complex geometry and low-textured regions in large scenes.
However, these methods heavily rely on 3D supervised information which is
costly and time-consuming to obtain in the real world. In this paper, we
propose a novel neural reconstruction method that reconstructs scenes without
3D supervision. We perform differentiable volume rendering for scene
reconstruction by using accessible 2D images as supervision. We impose geometry
to improve the reconstruction quality of complex geometry regions in the
scenes, and impose plane constraints to improve the reconstruction quality of
low-textured regions in the scenes. Specifically, we introduce a signed
distance function (SDF) field, a color field, and a probability field to
represent the scene, and optimize the fields under the differentiable ray
marching to reconstruct the scene. Besides, we impose geometric constraints
that project 3D points on the surface to similar-looking regions with similar
features in different views. We also impose plane constraints to make large
planes keep parallel or vertical to the wall or floor. These two constraints
help to reconstruct accurate and smooth geometry structures of the scene.
Without 3D supervision information, our method achieves competitive
reconstruction compared with some existing methods that use 3D information as
supervision on the ScanNet dataset.

本文提出了一种新的神经重建方法，使用可访问的 2D 图像作为监督来进行 3D 场景重建，其中通过引入二进制带注符号距离函数、色彩场和概率场等方法实现了场景的重建，并引入了几何和平面约束以实现对场景复杂几何区域和低纹理区域的重建。

无 3D 监督的多视角图像神经三维场景重建

Neural 3D Scene Reconstruction from Multi-view Images without 3D  Supervision

Recently, differentiable volume rendering in neural radiance fields (NeRF)
has gained a lot of popularity, and its variants have attained many impressive
results. However, existing methods usually assume the scene is a homogeneous
volume so that a ray is cast along the straight path. In this work, the scene
is instead a heterogeneous volume with a piecewise-constant refractive index,
where the path will be curved if it intersects the different refractive
indices. For novel view synthesis of refractive objects, our NeRF-based
framework aims to optimize the radiance fields of bounded volume and boundary
from multi-view posed images with refractive object silhouettes. To tackle this
challenging problem, the refractive index of a scene is reconstructed from
silhouettes. Given the refractive index, we extend the stratified and
hierarchical sampling techniques in NeRF to allow drawing samples along a
curved path tracked by the Eikonal equation. The results indicate that our
framework outperforms the state-of-the-art method both quantitatively and
qualitatively, demonstrating better performance on the perceptual similarity
metric and an apparent improvement in the rendering quality on several
synthetic and real scenes.

本文主要介绍了一种基于神经辐射场 (NeRF) 的框架，用于优化被限定的带折射物体边界体积的辐射场，以构建折射物体的新视图综合。作者使用分层和分级抽样技术，并使用 Eikonal 方程跟踪沿曲线路径绘制样本，演示了本框架在多个场景中均比最先进的方法具有更好的感知相似性和渲染质量显着提高。

为折射物采样神经辐射场

Sampling Neural Radiance Fields for Refractive Objects

Recent work has demonstrated that volumetric scene representations combined
with differentiable volume rendering can enable photo-realistic rendering for
challenging scenes that mesh reconstruction fails on. However, these methods
entangle geometry and appearance in a "black-box" volume that cannot be edited.
Instead, we present an approach that explicitly disentangles
geometry--represented as a continuous 3D volume--from appearance--represented
as a continuous 2D texture map. We achieve this by introducing a 3D-to-2D
texture mapping (or surface parameterization) network into volumetric
representations. We constrain this texture mapping network using an additional
2D-to-3D inverse mapping network and a novel cycle consistency loss to make 3D
surface points map to 2D texture points that map back to the original 3D
points. We demonstrate that this representation can be reconstructed using only
multi-view image supervision and generates high-quality rendering results. More
importantly, by separating geometry and texture, we allow users to edit
appearance by simply editing 2D texture maps.

该研究介绍了一种通过将几何图形和外观区分开来的方法，使用体积场景表示和可微分体积渲染，利用表面参数化网络以及循环一致性损失等技术，生成高质量的渲染结果，且可直接通过编辑 2D 纹理贴图来修改外观。