外观
论文arXiv 2512.0368713 天前
主动视觉感知：机遇与挑战
原文链接：Active Visual Perception: Opportunities and Challenges（arXiv 2512.03687 · 发布日期：2026-10-15）
Active Visual Perception: Opportunities and Challenges
本文综述主动视觉感知，提出闭环分析与评估框架，并从数据集、交互环境和物理平台讨论验证方式。
核心要点
- 针对主动感知定义和收益评估不一致，梳理其功能范围以便跨任务比较。
- 框架围绕所需证据、可获证动作、观测价值评估及下一步感知动作选择展开。
- 评估讨论数据集、交互环境和物理平台如何支持比较，并关联下游任务结果与代价风险。
- 指出观测价值预测不保证可行获证，未来需在物理和资源约束下改进估值与规划。
验证范围与局限：本文为综述，验证范围限于对现有数据集、交互环境和物理平台评估方式的分析。
英文摘要 (Abstract)
Active visual perception enables an intelligent system to improve perception by controlling how visual evidence is acquired. However, differences in how studies define active perception and evaluate its benefits make approaches difficult to compare across tasks. This review first clarifies the field’s functional scope by examining whether an action is selected for its expected effect on subsequent visual evidence and whether the resulting observation informs a later decision. This definition distinguishes active observation acquisition from processing fixed inputs and guides the selection of representative literature. Within this scope, this review develops an analytical framework organized around four questions. It begins with what evidence the task requires, examines which actions can obtain that evidence and how a candidate observation should be valued, and considers how the next sensing action should be selected and executed. New observations then update the perceptual state and guide subsequent decisions, forming a closed loop that provides a common basis for comparing methods across applications. To examine how their benefits can be demonstrated, this review assesses whether existing datasets support observation selection, how interactive environments enable controlled comparisons, and what physical platforms reveal about execution feasibility. It further examines how baseline comparisons can establish whether adapting observations is necessary and whether informed selection offers benefits beyond simpler sensing strategies. These considerations underpin an evaluation framework that links improvements in visual evidence to downstream task outcomes while accounting for acquisition costs and risks. Its core value is to help isolate the contribution of sensing decisions and determine whether their benefits justify the resources required. The analysis concludes that predicted observation value does not guarantee that useful evidence can be acquired through feasible actions, while task success alone cannot establish the benefit of active observation selection. Further progress requires reliable value estimation and planning over longer decision sequences under physical and resource constraints. It also requires grounding semantic reasoning in acquired evidence and maintaining effective collaboration under imperfect communication, with these capabilities verified through reproducible evaluation of the complete perception and action loop.
作者：Yian Li, Xiaoyu Guo, Hao Zhang, Jiayu Chen, Shuiwang Li, Xiaowei Dai
来源
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