The aim of this work is to establish how accurately a recent semantic-based
foveal active perception model is able to complete visual tasks that are
regularly performed by humans, namely, scene exploration and visual search.
This model exploits the ability of current object detectors to localize and
classify a large number of object classes and to update a semantic description
of a scene across multiple fixations. It has been used previously in scene
exploration tasks. In this paper, we revisit the model and extend its
application to visual search tasks. To illustrate the benefits of using
semantic information in scene exploration and visual search tasks, we compare
its performance against traditional saliency-based models. In the task of scene
exploration, the semantic-based method demonstrates superior performance
compared to the traditional saliency-based model in accurately representing the
semantic information present in the visual scene. In visual search experiments,
searching for instances of a target class in a visual field containing multiple
distractors shows superior performance compared to the saliency-driven model
and a random gaze selection algorithm. Our results demonstrate that semantic
information, from the top-down, influences visual exploration and search tasks
significantly, suggesting a potential area of research for integrating it with
traditional bottom-up cues.

通过使用语义信息，可以在场景探索和视觉搜索任务中有效地完成视觉任务，从而证明了语义模型在视觉任务中的优越性。

基于语义的人形机器视觉任务的主动感知技术与视点焦点传感器

Semantic-Based Active Perception for Humanoid Visual Tasks with Foveal  Sensors

Neural Radiance Fields (NeRF) have quickly become the primary approach for 3D
reconstruction and novel view synthesis in recent years due to their remarkable
performance. Despite the huge interest in NeRF methods, a practical use case of
NeRFs has largely been ignored; the exploration of the scene space modelled by
a NeRF. In this paper, for the first time in the literature, we propose and
formally define the scene exploration framework as the efficient discovery of
NeRF model inputs (i.e. coordinates and viewing angles), using which one can
render novel views that adhere to user-selected criteria. To remedy the lack of
approaches addressing scene exploration, we first propose two baseline methods
called Guided-Random Search (GRS) and Pose Interpolation-based Search (PIBS).
We then cast scene exploration as an optimization problem, and propose the
criteria-agnostic Evolution-Guided Pose Search (EGPS) for efficient
exploration. We test all three approaches with various criteria (e.g. saliency
maximization, image quality maximization, photo-composition quality
improvement) and show that our EGPS performs more favourably than other
baselines. We finally highlight key points and limitations, and outline
directions for future research in scene exploration.

这篇论文介绍了一种场景探索的框架，通过使用神经辐射场（NeRF）模型的输入信息，提出了两种基线方法和一种准则不受限的进化导向姿态搜索方法，实现了对用户选择准则的新视角合成，最终通过实验证明了这种方法优于其他基线方法，并提出了未来场景探索研究的方向。

寻找沃尔多：朝着 NeRF 场景空间的高效探索

Finding Waldo: Towards Efficient Exploration of NeRF Scene Space

Object finding in clutter is a skill that requires perception of the
environment and in many cases physical interaction. In robotics, interactive
perception defines a set of algorithms that leverage actions to improve the
perception of the environment, and vice versa use perception to guide the next
action. Scene interactions are difficult to model, therefore, most of the
current systems use predefined heuristics. This limits their ability to
efficiently search for the target object in a complex environment. In order to
remove heuristics and the need for explicit models of the interactions, in this
work we propose a reinforcement learning based active and interactive
perception system for scene exploration and object search. We evaluate our work
both in simulated and in real-world experiments using a robotic manipulator
equipped with an RGB and a depth camera, and compare our system to two
baselines. The results indicate that our approach, trained in simulation only,
transfers smoothly to reality and can solve the object finding task efficiently
and with more than 88% success rate.

研究了基于强化学习的互动感知系统在场景探索和物体搜索方面的应用，结果表明该方法不再需要预定义的启发式模型，可在模拟和现实世界中有效地解决这一任务。