We present Aria Everyday Activities (AEA) Dataset, an egocentric multimodal
open dataset recorded using Project Aria glasses. AEA contains 143 daily
activity sequences recorded by multiple wearers in five geographically diverse
indoor locations. Each of the recording contains multimodal sensor data
recorded through the Project Aria glasses. In addition, AEA provides machine
perception data including high frequency globally aligned 3D trajectories,
scene point cloud, per-frame 3D eye gaze vector and time aligned speech
transcription. In this paper, we demonstrate a few exemplar research
applications enabled by this dataset, including neural scene reconstruction and
prompted segmentation. AEA is an open source dataset that can be downloaded
from projectaria.com. We are also providing open-source implementations and
examples of how to use the dataset in Project Aria Tools.

Aria Everyday Activities (AEA) Dataset 是一个多模态的开放式数据集，记录了由多个佩戴者在五个不同地理位置的室内场景中进行的 143 个日常活动序列。该数据集通过 Project Aria 眼镜记录了多模态传感器数据，并提供了机器感知数据，包括高频全局对齐的 3D 轨迹、场景点云、每帧的 3D 凝视向量和时间对齐的语音转录。本文还展示了该数据集支持的一些示例研究应用，包括神经场景重建和提示分割。AEA 是一个开源数据集，可从 projectaria.com 下载，并提供了 Project Aria 工具的开源实现和示例。

Aria 日常活动数据集

Aria Everyday Activities Dataset

In this work, we introduce SCALAR-NeRF, a novel framework tailored for
scalable large-scale neural scene reconstruction. We structure the neural
representation as an encoder-decoder architecture, where the encoder processes
3D point coordinates to produce encoded features, and the decoder generates
geometric values that include volume densities of signed distances and colors.
Our approach first trains a coarse global model on the entire image dataset.
Subsequently, we partition the images into smaller blocks using KMeans with
each block being modeled by a dedicated local model. We enhance the overlapping
regions across different blocks by scaling up the bounding boxes of each local
block. Notably, the decoder from the global model is shared across distinct
blocks and therefore promoting alignment in the feature space of local
encoders. We propose an effective and efficient methodology to fuse the outputs
from these local models to attain the final reconstruction. Employing this
refined coarse-to-fine strategy, our method outperforms state-of-the-art NeRF
methods and demonstrates scalability for large-scale scene reconstruction. The
code will be available on our project page at
this https URL

SCALAR-NeRF 是一个用于大规模神经场景重建的新框架，它采用编码器 - 解码器架构，通过处理 3D 点坐标生成编码特征，并生成包括有符号距离和颜色的几何值。通过在整个图像数据集上训练一个粗略全局模型，将图像分割成较小的块，每个块采用专用的局部模型。使用本地模型的特征空间中的全局模型解码器来提升不同块之间的重叠区域。通过有效且高效地融合这些局部模型的输出，我们的方法在大规模场景重建方面优于现有的 NeRF 方法，并展示了可扩展性。

SCALAR-NeRF: 可扩展的大规模神经辐射场景重建

SCALAR-NeRF: SCAlable LARge-scale Neural Radiance Fields for Scene  Reconstruction

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。