It is desirable to create 3D object models and 3D maps from 2D input images
for applications such as navigation, virtual tourism, and urban planning. The
traditional methods of creating 3D maps, (such as photogrammetry), require a
large number of images and odometry. Additionally, traditional methods have
difficulty with reflective surfaces and specular reflections; windows and
chrome in the scene can be problematic. Google Road View is a familiar
application, which uses traditional methods to fuse a collection of 2D input
images into the illusion of a 3D map. However, Google Road View does not create
an actual 3D object model, only a collection of views. The objective of this
work is to create an actual 3D object model using updated techniques. Neural
Radiance Fields (NeRF[1]) has emerged as a potential solution, offering the
capability to produce more precise and intricate 3D maps. Gaussian Splatting[4]
is another contemporary technique. This investigation compares Neural Radiance
Fields to Gaussian Splatting, and describes some of their inner workings. Our
primary contribution is a method for improving the results of the 3D
reconstructed models. Our results indicate that Gaussian Splatting was superior
to the NeRF technique.

使用更新的技术创建实际的三维物体模型，比较神经辐射场与高斯分层的内部工作，并提出改进三维重建模型结果的方法，结果显示高斯分层技术优于 NeRF 技术。

利用辐射场创建无缝 3D 地图

Creating Seamless 3D Maps Using Radiance Fields

DIVeR builds on the key ideas of NeRF and its variants -- density models and
volume rendering -- to learn 3D object models that can be rendered
realistically from small numbers of images. In contrast to all previous NeRF
methods, DIVeR uses deterministic rather than stochastic estimates of the
volume rendering integral. DIVeR's representation is a voxel based field of
features. To compute the volume rendering integral, a ray is broken into
intervals, one per voxel; components of the volume rendering integral are
estimated from the features for each interval using an MLP, and the components
are aggregated. As a result, DIVeR can render thin translucent structures that
are missed by other integrators. Furthermore, DIVeR's representation has
semantics that is relatively exposed compared to other such methods -- moving
feature vectors around in the voxel space results in natural edits. Extensive
qualitative and quantitative comparisons to current state-of-the-art methods
show that DIVeR produces models that (1) render at or above state-of-the-art
quality, (2) are very small without being baked, (3) render very fast without
being baked, and (4) can be edited in natural ways.

DIVeR 使用确定性方法替代随机性，使用 MLP 从每个体素的特征中估算渲染积分的组成部分，并进行汇总，从而生成可以在自然方式下编辑的体素基础场，这使其能够更好地渲染细胞透明结构。

DIVeR: 用于体渲染的实时和准确的神经辐射场，采用确定性积分

DIVeR: Real-time and Accurate Neural Radiance Fields with Deterministic  Integration for Volume Rendering

A crucial ability of human intelligence is to build up models of individual
3D objects from partial scene observations. Recent works achieve object-centric
generation but without the ability to infer the representation, or achieve 3D
scene representation learning but without object-centric compositionality.
Therefore, learning to represent and render 3D scenes with object-centric
compositionality remains elusive. In this paper, we propose a probabilistic
generative model for learning to build modular and compositional 3D object
models from partial observations of a multi-object scene. The proposed model
can (i) infer the 3D object representations by learning to search and group
object areas and also (ii) render from an arbitrary viewpoint not only
individual objects but also the full scene by compositing the objects. The
entire learning process is unsupervised and end-to-end. In experiments, in
addition to generation quality, we also demonstrate that the learned
representation permits object-wise manipulation and novel scene generation, and
generalizes to various settings. Results can be found on our project website:
this https URL

本文提出了一种概率生成模型，用于学习从多个物体场景的部分观察中构建模块化和组合化的三维物体模型，该模型可以推断 3D 物体表示并从任意视角呈现场景。

ROOTS: 3D 场景的对象中心表示和渲染

ROOTS: Object-Centric Representation and Rendering of 3D Scenes

We present an approach to synthesize highly photorealistic images of 3D
object models, which we use to train a convolutional neural network for
detecting the objects in real images. The proposed approach has three key
ingredients: (1) 3D object models are rendered in 3D models of complete scenes
with realistic materials and lighting, (2) plausible geometric configuration of
objects and cameras in a scene is generated using physics simulations, and (3)
high photorealism of the synthesized images achieved by physically based
rendering. When trained on images synthesized by the proposed approach, the
Faster R-CNN object detector achieves a 24% absolute improvement of mAP@.75IoU
on Rutgers APC and 11% on LineMod-Occluded datasets, compared to a baseline
where the training images are synthesized by rendering object models on top of
random photographs. This work is a step towards being able to effectively train
object detectors without capturing or annotating any real images. A dataset of
600K synthetic images with ground truth annotations for various computer vision
tasks will be released on the project website: thodan.github.io/objectsynth.

该论文提出了一种方法来合成高度逼真的三维物体模型图像，并利用这些图像训练卷积神经网络以在真实图像中检测物体。