Neural implicit surface representation methods have recently shown impressive
3D reconstruction results. However, existing solutions struggle to reconstruct
urban outdoor scenes due to their large, unbounded, and highly detailed nature.
Hence, to achieve accurate reconstructions, additional supervision data such as
LiDAR, strong geometric priors, and long training times are required. To tackle
such issues, we present SCILLA, a new hybrid implicit surface learning method
to reconstruct large driving scenes from 2D images. SCILLA's hybrid
architecture models two separate implicit fields: one for the volumetric
density and another for the signed distance to the surface. To accurately
represent urban outdoor scenarios, we introduce a novel volume-rendering
strategy that relies on self-supervised probabilistic density estimation to
sample points near the surface and transition progressively from volumetric to
surface representation. Our solution permits a proper and fast initialization
of the signed distance field without relying on any geometric prior on the
scene, compared to concurrent methods. By conducting extensive experiments on
four outdoor driving datasets, we show that SCILLA can learn an accurate and
detailed 3D surface scene representation in various urban scenarios while being
two times faster to train compared to previous state-of-the-art solutions.

SCILLA 是一种新的混合隐式表面学习方法，用于从 2D 图像中重建大型驾驶场景，通过引入自监督概率密度估计的体素渲染策略，能够在不依赖场景几何先验的情况下，快速初始化有关场景的有符号距离场，并能在各种城市场景中学习准确和详细的三维表面场景表示。

SCILLA: 大型城市地区表面隐式学习的体积混合解决方案

SCILLA: SurfaCe Implicit Learning for Large Urban Area, a volumetric  hybrid solution

Occlusion is a common issue in 3D reconstruction from RGB-D videos, often
blocking the complete reconstruction of objects and presenting an ongoing
problem. In this paper, we propose a novel framework, empowered by a 2D
diffusion-based in-painting model, to reconstruct complete surfaces for the
hidden parts of objects. Specifically, we utilize a pre-trained diffusion model
to fill in the hidden areas of 2D images. Then we use these in-painted images
to optimize a neural implicit surface representation for each instance for 3D
reconstruction. Since creating the in-painting masks needed for this process is
tricky, we adopt a human-in-the-loop strategy that involves very little human
engagement to generate high-quality masks. Moreover, some parts of objects can
be totally hidden because the videos are usually shot from limited
perspectives. To ensure recovering these invisible areas, we develop a cascaded
network architecture for predicting signed distance field, making use of
different frequency bands of positional encoding and maintaining overall
smoothness. Besides the commonly used rendering loss, Eikonal loss, and
silhouette loss, we adopt a CLIP-based semantic consistency loss to guide the
surface from unseen camera angles. Experiments on ScanNet scenes show that our
proposed framework achieves state-of-the-art accuracy and completeness in
object-level reconstruction from scene-level RGB-D videos.

本文提出了一个新颖的框架，利用基于扩散的 2D 修复模型，通过填补物体隐藏部分的 2D 图像来重建完整的表面，以及通过神经隐式表面表示优化每个实例的 3D 重建。实验结果表明，我们的方法在从 RGB-D 视频中进行物体级重建时达到了最先进的准确性和完整性。

O^2-Recon：利用预训练的 2D 扩散模型完成场景中被遮挡物体的 3D 重建

O^2-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene  with a Pre-trained 2D Diffusion Model

We present a method for 3D reconstruction only using calibrated multi-view
surface azimuth maps. Our method, multi-view azimuth stereo, is effective for
textureless or specular surfaces, which are difficult for conventional
multi-view stereo methods. We introduce the concept of tangent space
consistency: Multi-view azimuth observations of a surface point should be
lifted to the same tangent space. Leveraging this consistency, we recover the
shape by optimizing a neural implicit surface representation. Our method
harnesses the robust azimuth estimation capabilities of photometric stereo
methods or polarization imaging while bypassing potentially complex zenith
angle estimation. Experiments using azimuth maps from various sources validate
the accurate shape recovery with our method, even without zenith angles.

本文提出了一种仅使用校准多视图表面方位地图进行三维重建的方法 —— 多视图方位立体。这种方法对于无纹理或具有镜面效果的表面来说非常有效，并通过引入切空间一致性概念，在使用光度立体方法或偏振成像时，利用切空间一致性以恢复形状，从而绕过了可能的复杂天顶角估计。实验表明，即使没有天顶角，使用来自各种来源的方位地图，我们的方法也能够验证准确的形状恢复。