An environment acoustic model represents how sound is transformed by the
physical characteristics of an indoor environment, for any given
source/receiver location. Traditional methods for constructing acoustic models
involve expensive and time-consuming collection of large quantities of acoustic
data at dense spatial locations in the space, or rely on privileged knowledge
of scene geometry to intelligently select acoustic data sampling locations. We
propose active acoustic sampling, a new task for efficiently building an
environment acoustic model of an unmapped environment in which a mobile agent
equipped with visual and acoustic sensors jointly constructs the environment
acoustic model and the occupancy map on-the-fly. We introduce ActiveRIR, a
reinforcement learning (RL) policy that leverages information from audio-visual
sensor streams to guide agent navigation and determine optimal acoustic data
sampling positions, yielding a high quality acoustic model of the environment
from a minimal set of acoustic samples. We train our policy with a novel RL
reward based on information gain in the environment acoustic model. Evaluating
on diverse unseen indoor environments from a state-of-the-art acoustic
simulation platform, ActiveRIR outperforms an array of methods--both
traditional navigation agents based on spatial novelty and visual exploration
as well as existing state-of-the-art methods.

通过使用视觉和声音传感器数据，我们提出了一种新的任务 —— 主动声学采样，其通过移动代理在实时环境中建立环境声学模型和占据地图，以及确定最佳声学数据采样位置，从而以最少的声学样本得到高质量的环境声学模型。我们通过基于环境声学模型中的信息增益的新型强化学习奖励来训练我们的策略，在来自先进声学模拟平台的多样未知室内环境上评估，发现 ActiveRIR 优于传统导航代理、基于空间新颖性和视觉探索的方法以及现有最先进的方法。

ActiveRIR: 主动音频视觉探索声学环境建模

ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment  Modeling

Collecting 3D object datasets involves a large amount of manual work and is
time consuming. Getting complete models of objects either requires a 3D scanner
that covers all the surfaces of an object or one needs to rotate it to
completely observe it. We present a system that incrementally builds a database
of objects as a mobile agent traverses a scene. Our approach requires no prior
knowledge of the shapes present in the scene. Object-like segments are
extracted from a global segmentation map, which is built online using the input
of segmented RGB-D images. These segments are stored in a database, matched
among each other, and merged with other previously observed instances. This
allows us to create and improve object models on the fly and to use these
merged models to reconstruct also unobserved parts of the scene. The database
contains each (potentially merged) object model only once, together with a set
of poses where it was observed. We evaluate our pipeline with one public
dataset, and on a newly created Google Tango dataset containing four indoor
scenes with some of the objects appearing multiple times, both within and
across scenes.

提出了一种使用移动代理逐步建立物体数据库的系统，从全局分割图中提取对象状的片段，并将其储存在数据库中，然后匹配和合并以创建和改进对象模型，该方法不需要场景中形状的先前知识。