cs.CVDec 3, 2025
SaveActive Visual Perception: Opportunities and Challenges
Organizations: College of Computer Science and Engineering, Guilin University of Technology, Guilin 541004, China · New Engineering Industry College, Putian University, Putian 351100, China
Abstract
Active visual perception enables an intelligent system to improve perception by controlling how visual evidence is acquired. However, differences in how active perception is defined and evaluated make approaches difficult to compare across tasks. This review clarifies the field's functional scope by distinguishing active observation acquisition from processing fixed inputs. It then organizes representative literature around four questions about what evidence the task requires, which actions can obtain it, how candidate observations should be valued, and how the next sensing action should be selected and executed. New observations update the perceptual state and guide subsequent decisions, forming a closed loop. To evaluate the benefits of active perception, the review examines datasets, interactive environments, physical platforms, and baseline comparisons, and develops an evaluation framework linking improvements in visual evidence to downstream task outcomes while accounting for acquisition costs and risks. 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.
Figures & tables
Figure 1: Conceptual framework of active visual perception linking perception targets, sensing actions, decision objectives, decision mechanisms, and application domains.
| Review | Primary Scope | Observation Control | Decision Emphasis | Evaluation / Constraints |
| Bajcsy et al. Bajcsy et al. (2018) | Conceptual foundations of active perception | What, where, when, and how to sense | Task and context dependent sensing | Resource constraints discussed conceptually |
| Chen et al. Chen et al. (2011) | Active vision in robotic systems | Sensor pose, configuration, and viewpoint planning | Uncertainty and task-oriented sensor planning | Robot configurations, applications, and method evaluation |
| Zeng et al. Zeng et al. (2020) | View planning for robotic active vision | Primarily sensor viewpoint and view sequence | View quality and planning strategies | Hardware conditions, scanning state, and planning efficiency |
| Ahmed et al. Ahmed et al. (2023) | Active and collaborative SLAM | Robot trajectory and control action selection | Pose and map uncertainty, information utility | Planning methods, deployment settings, and SLAM performance |
| This review | Cross-task active observation acquisition | Viewpoint, gaze, sensing, interaction, and coordination | Required evidence, observation value, and action selection | Evidence improvement, task improvement, feasibility, and resource cost |
Table 1: Comparison between representative previous reviews and the analytical scope of this review.
Figure 2: Closed-loop process linking perceptual needs, sensing actions, action evaluation, execution, and state updates.
Figure 3: Representative milestones in active visual perception organized by observation control mechanism.
| Literature Area | Representative Search Terms |
| Foundations and prior reviews | Active vision; active visual perception; active sensing; revisiting active perception |
| Next best view and active reconstruction | Next best view; active 3D reconstruction; active view planning; viewpoint selection for reconstruction |
| Active SLAM and exploration | Active SLAM; active visual SLAM; information gain for active SLAM; semantic active SLAM; active exploration |
| Visual search, recognition, and navigation | Active visual search; embodied visual search; active object recognition; active perception for object navigation |
| Camera control, gaze, and tracking | Active camera control; pan tilt zoom camera tracking; gaze control; active viewpoint tracking; multi-camera tracking |
| Robotic manipulation and interactive perception | Active perception for robotic manipulation; manipulation under occlusion; interactive perception; active viewpoint selection for manipulation |
Table 2: Literature areas and representative search terms used in this review.
| Decision Mechanism and Representative Studies | Required Information | Suitable Sensing Decisions | Evaluation Setting and Main Limitation |
| Rule-based and geometric Frintrop and Jensfelt (2008) ; Rivlin and Rotstein (2000) ; Dunn and Frahm (2009) ; Yang and Waslander (2022) | Designed states, geometric models, visibility, and reachability | Gaze control, camera pose, viewpoint, and platform motion | Simulation evidence. Evaluation is limited to simulation, where performance depends on designed rules, geometric accuracy, and idealized visibility. Physical-platform validation. Not reported; robustness to calibration errors, dynamic occlusion, material effects, and actuation uncertainty remains unverified. |
| Probabilistic and belief-based Laporte and Arbel (2006) ; Kim and Eustice (2012) ; Kitanov and Indelman (2024) | State beliefs, observation likelihoods, and uncertainty models | Viewpoint, revisit action, path, and sensing sequence | Simulation evidence. Evaluation is limited to simulation, where computation increases with belief dimensionality and planning horizon and depends on assumed noise models. Physical-platform validation. Not reported; robustness to sensor failures, likelihood miscalibration, measurement noise, and real-time constraints remains unverified. |
| Information-based optimization Mu et al. (2016) ; Lauri et al. (2020) ; Bai et al. (2024) | Calibrated state uncertainty and predicted observation updates | Viewpoint, waypoint, sensor, and measurement selection | Simulation evidence. Evaluation is limited to simulation, where observation values inherit errors from idealized belief and sensor models. Physical-platform validation. Not reported; whether predicted information gain is preserved under sensor noise, model mismatch, and execution delay remains unverified. |
| Search and receding horizon planning Leung et al. (2006) ; Chen et al. (2020) ; Wang et al. (2023) ; Taioli et al. (2024) | Candidate actions, transition models, and predicted future observations | View sequences, exploration routes, and continuous camera motion | Simulation evidence. Evaluation is limited to simulation, where computation grows with the action space and prediction depth and future observations are often idealized. Physical-platform validation. Not reported; robustness to planning latency, motion uncertainty, dynamic obstacles, and reachability constraints remains unverified. |
| Supervised and imitation learning Mendoza et al. (2020) ; Xiong et al. (2025) ; Chen et al. (2024) | View labels, expert values, or demonstrations | Viewpoint, gaze, zoom, and camera behavior | Simulation evidence. Evaluation is limited to simulation, where policies inherit training-data coverage limits and may overfit simulated appearance or action discretization. Physical-platform validation. Not reported; robustness to changes in lighting, camera calibration, motion blur, and actuator response remains unverified. |
| Reinforcement learning Peralta et al. (2020) ; Placed and Castellanos (2020) ; Bartolomei et al. (2021) ; Huang et al. (2023) | Interaction experience, rewards, and environment dynamics | Path, viewpoint, gaze, and task coupled sensing actions | Simulation evidence. Evaluation is limited to simulation, where policies remain sensitive to reward design and may exploit simulator-specific appearance or dynamics. Physical-platform validation. Not reported; sim-to-real transfer, safe exploration, control delay, and reliability under physical disturbances remain unverified. |
Table 3: Comparison of representative mechanisms for selecting sensing actions in active visual perception. The reported simulation limitations and the status of physical-platform validation are distinguished.
| Task | Representative Resources | Support for Active Evaluation | Main Limitation |
| Active reconstruction Dunn and Frahm (2009) ; Peralta et al. (2020) ; Chen et al. (2024) ; Dhami et al. (2023) | Synthetic meshes, ShapeNet style objects, Middlebury data, custom CAD models, and real object collections | Accurate geometry and controllable views support coverage, completeness, and view contribution analysis | Viewpoints are often predefined and ignore reachability, collision, and motion error |
| Active localization and exploration Ahmed et al. (2023) ; Placed et al. (2022) ; Bonetto et al. (2022) | KITTI, Freiburg, Intel Research Lab, MIT Stata Center, custom simulators, and mobile robots | Interactive settings support route, revisit, coverage, and loop closure decisions | Recorded trajectories cannot evaluate action selection, while custom environments limit direct comparison |
| Search and navigation Ammirato et al. (2017) ; Savva et al. (2019) ; Deitke et al. (2020) | Active Vision Dataset, Habitat, RoboTHOR, and related indoor embodied environments | Policies can change position and gaze and acquire task dependent observations | Results depend on annotations, action discretization, sensor assumptions, and the perception backbone |
| Active tracking Cai and Medioni (2016) ; Chen et al. (2024) ; Casao et al. (2024) | Controllable camera networks, pan tilt zoom systems, simulated targets, and physical video platforms | Camera orientation, assignment, and target handover can affect future visibility | Custom target dynamics and camera models reduce comparability across studies |
| Robotic manipulation Jauhri et al. (2024) ; He et al. (2026) ; Liu et al. (2026) | Tabletop simulation, physical manipulators, EFM10, ActiveViewPose 200K, and ActiveManip Bench | Viewpoint and interaction actions can be evaluated together with task execution | Perception, sensing, planning, and control gains remain difficult to separate |
| Collaborative perception Hu et al. (2022) ; Yang et al. (2023) ; Huang et al. (2024) ; Bai et al. (2024) | V2X-Sim, OPV2V, V2XSet, DAIR-V2X, multirobot maps, and camera networks | Supports complementary observation, message selection, and communication cost analysis | Accurate poses, synchronization, and stable communication can overestimate collaborative gains |
Table 4: Representative datasets and interactive environments used in active visual perception.
| Evaluation Question | Required Baseline or Measurement | What It Reveals | Common Confounding Factor |
| Is active sensing necessary? | Passive or fixed observation baseline | Whether changing the observation improves perception or the downstream task | The active method may also use a stronger perception model |
| Is the selection rule informative? | Random and uniform action baselines | Whether task or uncertainty information improves action choice | Different candidate action sets may favour one method |
| Does planning horizon matter? | Greedy or immediate utility baseline | Whether reasoning about later observations improves the result | Longer planning may use more computation or privileged prediction |
| Did the observation itself improve? | Visibility, coverage, uncertainty, valid depth, or pose quality before and after the action | The direct contribution of the acquired evidence | The metric may be poorly calibrated or unrelated to the task |
| Did the task improve? | Reconstruction, localization, search, tracking, manipulation, or detection performance | Whether the new evidence changes the final task outcome | Control quality and the perception backbone also affect the result |
| Was the improvement efficient? | View count, path length, action count, time, energy, computation, and communication | The resources required to obtain the reported improvement | A combined score can hide which resource caused the difference |
Table 5: Baselines and measurements required for closed loop evaluation of active visual perception.
Figure 4: Representative active perception processes in robotic manipulation, collaborative driving, and UAV-assisted disaster rescue. In all subfigures, the yellow arrows point to the target, while the boxes in the lower-right corner of panels (a) and (b) show enlarged local views of the region observed from the current viewpoint. (a) Next-best-view selection for robotic manipulation, where the current and candidate viewpoints are distinguished by viewing cones with different colors; the asterisk marks the target, and the check mark indicates the selected viewpoint. (b) Collaborative driving, where the green dashed arrows indicate the sharing of complementary perceptual information between vehicles and between vehicles and the roadside sensor. (c) UAV-assisted disaster rescue, where the dashed arrows schematically illustrate three candidate search paths for acquiring task-relevant visual observations; the green dashed path indicates the selected search path.
| Application | Information Deficit | Controlled Action | Expected Evidence | Dominant Constraints |
| Robotic manipulation | Hidden grasp region, uncertain pose, or unknown object state | Wrist or head motion, base motion, active-view arm, or object interaction | Clearer affordance, improved pose, or interaction-generated state evidence | Collision, reachability, irreversible action, latency, and task coupling |
| Autonomous driving | Occluded road user, weak visibility, or incomplete local sensing | Camera orientation, adaptive resolution, modality selection, or collaborative query | Target confirmation, complementary geometry, or remote hazard evidence | Real-time response, safety, weather, synchronization, and pose error |
| Human-centered systems | Occluded body, uncertain gesture, or missing shared-task evidence | Gaze, camera orientation, robot repositioning, or selective resolution | Clearer pose, attention, intention, or shared-object state | Personal space, privacy, interpretability, consent, and social acceptance |
| Surveillance and security | Blind spot, target exit, identity ambiguity, or limited resolution | Pan, tilt, zoom, camera assignment, or target handover | Centered target, identity-preserving sequence, or complementary views | Target competition, control delay, privacy, and operator accountability |
| UAV search and rescue | Unknown layout, hidden victim, or inaccessible region | Flight path, altitude, orientation, revisit, or team allocation | Victim evidence, damage map, route information, or wide-area coverage | Battery, collision, urgency, wind, and communication |
| Environmental monitoring | Sparse anomaly, changing conditions, or uneven target distribution | Route, viewpoint, sensor schedule, or spatially varying sampling | Focused anomaly evidence and long-term change observations | Energy, weather, revisit fairness, and long-term autonomy |
Table 6: Perception–action characteristics of representative application settings.
Figure 5: Interacting challenges in the active visual perception loop.
| Challenge | Why It Arises | Consequence for Active Perception | Unresolved Gap |
| State estimation in incomplete and changing scenes | Partial observations, semantic errors, poorly calibrated confidence, and changing occlusion | The system may select actions from an incorrect or outdated state and reinforce its initial error | State representations that preserve task relevance, temporal validity, and calibrated geometric and semantic uncertainty |
| Action selection under limited time and resources | Long-term action dependence, large action spaces, and expensive prediction and feasibility checking | Greedy decisions can waste the sensing budget, while more extensive planning may become too slow for the task | Planning methods that adapt their horizon and computational effort to task urgency and available resources |
| Executable sensing on physical platforms | Ideal viewpoints may ignore reachability, embodiment, motion error, and interference with the task action | An informative viewpoint may be unreachable, unsafe, or detrimental to the subsequent operation | Joint reasoning about observation value, platform dynamics, task feasibility, and transfer from simulation to physical systems |
| Collaborative perception under imperfect communication | Limited bandwidth, transmission delay, pose disagreement, and agent or sensor failure | Shared evidence may become outdated or misaligned, while poor coordination causes redundant sensing or observation gaps | Distributed policies that coordinate motion, sensing, and message selection while remaining reliable under intermittent communication |
| Safety, privacy, and system responsibility | Active movement, physical interaction, camera redirection, zoom, and persistent observation can affect people and the environment | The system may cause collision, object damage, privacy intrusion, or unjustified actions under unresolved uncertainty | Risk aware stopping, minimum necessary observation, human oversight, and interpretable reasons for sensing actions |
| Benchmarking, reproducibility, and fair comparison | Studies use different scenes, action spaces, sensing budgets, perception backbones, and model versions | Reported results are difficult to compare, and the source of a performance gain or failure often remains unclear | Shared closed-loop settings, transparent system configurations, and corresponding simulation and physical evaluations |
Table 7: Main challenges and unresolved gaps in active visual perception.
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