Dynamic Object Suppression

Updated 24 October 2025

Dynamic object suppression is a set of techniques that distinguish static scene elements from dynamic ones using robust feature extraction and clustering.
It employs methods such as pixel-level inpainting, multi-camera depth estimation, and QUBO-based optimization to remove moving artifacts effectively.
These approaches enhance practical applications like static scene reconstruction, improved SLAM accuracy, and reliable object detection in dynamic environments.

Dynamic object suppression refers to a range of algorithmic and system-level techniques aimed at identifying, removing, suppressing, or reconstructing the effects of moving objects (dynamic entities) in sensor data, images, or maps produced by autonomous systems, scene reconstruction frameworks, and perception pipelines. The underlying motivation is to produce outputs—such as cleaned images, static environment maps, or robust detection results—where transient or undesired motion-induced artifacts do not corrupt performance-critical tasks like localization, recognition, or planning.

1. Principles and Motivations

A central challenge in multi-view perception, robotics, and computer vision is that scenes often contain both static and dynamic elements. When dynamic objects (e.g., moving people, vehicles) occlude parts of the environment or are incorporated into maps, these elements can lead to incorrect associations, degraded localization or SLAM accuracy, or spurious artifacts in scene reconstructions.

The core goals of dynamic object suppression are:

To robustly distinguish between regions or features corresponding to static scene content vs. dynamic entities.
To remove, replace, or ignore dynamic components in ways that do not introduce additional artifacts, and in some cases, to reconstruct the occluded static scene as if the dynamics had never been present.
To achieve this under practical constraints, such as online processing, use of handheld sensors (non-static viewpoints), or with minimal human supervision.

Applications include: static scene reconstruction from multi-view photographs, creation of long-term SLAM maps for autonomous robots, panoramic image stitching, robust object detection in crowded environments, and improving the reliability of perception pipelines in dynamic settings.

2. Methodological Approaches

Dynamic object suppression spans a spectrum of techniques across modalities and use cases.

Pixel- and Patch-Level Suppression in Multi-View Images

Approaches such as simultaneous detection and removal in multi-view images operate by:

Extracting dense feature descriptors (e.g., SIFT, CIE Lab mean) from each pixel/patch in a reference image and establishing dense correspondences to multiple source images, even when viewpoint changes are significant.
Quantifying similarity via appearance and geometric consistency (e.g., using fundamental matrix and measures like the squared Sampson distance).
Clustering candidate correspondences using metrics that aggregate both color and high-dimensional gradient similarity, then thresholding a matching score to classify pixels as static or dynamic.
For dynamic (occluding) pixels, filling using patch-based replacements sourced from unoccluded regions in other images, optimizing for spatial coherence at patch boundaries with objective functions that include appearance and HoG similarity.
Iteratively and bidirectionally scanning and updating the reference image and feature maps until no dynamic pixels remain, thereby achieving artifact-free inpainting of background (Kanojia et al., 2019).

Light Field and Multi-Camera Array Refocusing

Techniques that leverage the redundancy of light-field or multi-camera arrangements perform suppression by:

Estimating scene depth via multi-view photometric consistency, then selectively integrating only those image rays confirmed as static (via semantic segmentation and MAP estimation in a graphical model).
Employing EM algorithms where segmentation and depth mutually inform one another, enabling precise “seeing through” dynamic foreground objects.
Resulting in artifact-free, refocused static background images usable for SLAM, navigation, and rendering in robotics (Kaveti et al., 2020).

Object Detection Pipeline Suppression

Object detection frameworks, particularly in crowded scenes, employ suppression mainly during non-maximum selection. Recent advances include:

Hashing-based NMS (HNMS), which discretizes bounding box parameters (width, height, center, offsets) into hash cells, retaining only the highest-scoring box per cell and cascading hashes to cover border cases. This reduces O(N²) complexity to O(N) without loss of accuracy (Wang et al., 2020).
Quadratic Unconstrained Binary Optimization (QUBO)-based suppression, casting detection as the selection of an optimal set of non-redundant boxes via maximization of a quadratic form over scores and overlaps. Quantum annealers or classical solvers are used. Soft scoring and adjustment terms enable improved performance under occlusion (Li et al., 2020, Yamamura et al., 5 Feb 2025).
NMS-free detection architectures (e.g., set-based, Dynamic Graph CNNs), in which object predictions are matched to ground truth via bipartite assignment (Hungarian matching), rendering suppression post-processing unnecessary (Wang et al., 2021).

Map-Based and SLAM-Oriented Suppression

For static environment mapping with moving objects:

Temporal and geometric consistency, motion estimation, and segmentation are employed to detect moving objects (e.g., via 3D object detection in point clouds).
Temporal analysis, such as observation time differences between ground and non-ground voxels, categorizes dynamic objects as “suddenly appearing” or “suddenly disappearing,” with retrieval-based voxel flagging and compensation for static misclassifications (Wu et al., 22 Jun 2024).
Multi-resolution and incremental free space estimation, as in FreeDOM, enables online scan-removal and back-end refinement with conservative thresholding and region-growing, outperforming traditional methods in F1-score and robustness (Li et al., 15 Apr 2025).
Integration with dynamic SLAM and the separate maintenance of submaps for dynamic objects vs. static background (e.g., with VDB-backed volumetric representations), supported by motion estimates in SE(3), allows accurate suppression and reconstruction of moving objects and free space for navigation (Wang et al., 30 Sep 2024).

Adversarial Suppression

Beyond removal and detection, suppression can be adversarial: learned, view-dependent adversarial patches attached to objects dynamically mislead object detectors, with patch selection changing in real-time as a function of the camera viewpoint (Hoory et al., 2020). Such attacks can achieve up to 90% suppression of detection across a wide angular range.

3. Key Technical Components

Feature Extraction and Correspondence

Dense correspondence is frequently established via robust descriptors (SIFT, CIE Lab, HoG) and geometric checks (e.g., fundamental matrix, Sampson distance). Clustering these correspondences by feature similarity is essential for identifying consistent (static) vs. inconsistent (dynamic) mappings (Kanojia et al., 2019).

Clustering and Scoring

Clustering algorithms (e.g., DBSCAN) and scoring metrics—weighted sums of appearance and geometric similarity—enable selection of the dominant cluster representing the static region for each pixel. These matching scores are thresholded for classification (Kanojia et al., 2019).

Patch Replacement and Inpainting

Patch-based filling is motivated by spatial coherence, exploiting overlapping neighbor agreement and orientation-invariant features. Inpainting is sometimes performed via deep video networks in the context of video-based SLAM pipelines (Uppala et al., 2023).

Semantic Segmentation

Semantic priors from networks like BodyPix are integrated into probabilistic frameworks to filter out rays from dynamic regions, reinforcing geometric consistency in multi-view and light field suppression (Kaveti et al., 2020).

Online suppression methods leverage per-voxel time-stamped observation histories, differential thresholds, and voxel-based retrieval strategies to efficiently and adaptively mark, restore, or clear dynamic content in real time during mapping (Wu et al., 22 Jun 2024, Li et al., 15 Apr 2025).

4. Performance Metrics and Empirical Results

Performance of dynamic object suppression is typically measured by:

Artifact-free removal in multi-view images, evaluated with visual consistency or region accuracy (e.g., via Jaccard index) (Kanojia et al., 2019).
Detection recall, precision, mean Average Precision (mAP), and mean Average Recall (mAR) relative to ground truth boxes in detector pipelines; for QUBO-based improvements, mAP and mAR gains reach up to 4.54 and 9.89 points, respectively, over SOTA QUBO techniques (Yamamura et al., 5 Feb 2025).
Rejection Rate (RR) and Preservation Rate (PR)—fractions of dynamic and static points correctly removed or retained in SLAM or mapping, aggregated as F₁ score; FreeDOM demonstrates an average F₁ improvement of 9.7% over baselines (Li et al., 15 Apr 2025).
Localization improvement, typically measured as reduction in RMSE of scan-to-map matching or pose estimation errors after suppression (Woo et al., 1 Jul 2024).
Throughput/efficiency, including O(N) complexity for HNMS on high-density images (Wang et al., 2020), and real-time performance (e.g., ~20 FPS for DynORecon (Wang et al., 30 Sep 2024), ~24 ms per image for QUBO suppression (Yamamura et al., 5 Feb 2025)).

5. Comparative Analysis and Unique Contributions

Dynamic object suppression research distinguishes itself via:

Simultaneous unified frameworks for detection and removal, eliminating the need for external user supervision or downstream artifact elimination (Kanojia et al., 2019).
Generalizability to non-static sensor arrangements (e.g., handheld cameras, robot platforms), with explicit geometric and temporal modeling.
Modular, learning-free online algorithms (e.g., observation time difference criterion, conservative free space estimation) that maintain computational efficiency and robustly adapt to new scenes (Wu et al., 22 Jun 2024, Li et al., 15 Apr 2025).
Use of appearance and confidence features (as in QAQS, QAQS‑C) that enable nuanced treatment of overlap suppression in crowded or occluded cases, reducing over-suppression by conventional NMS (Yamamura et al., 5 Feb 2025).
Dynamic reconstruction architectures (e.g., DynORecon) which maintain independent submaps per moving object, keeping static maps clean for navigation and enabling accurate motion-integrated object modeling (Wang et al., 30 Sep 2024).
Application of adversarial optimization to actively, dynamically suppress object detector output, highlighting detector vulnerabilities (Hoory et al., 2020).

A table summarizing selected performance metrics from the literature:

Method/Reference	Key Metric(s)	Notable Result
Multi-view sim. removal (Kanojia et al., 2019)	Visual artifact removal	Artifact-free static reconstructions in real scenes
Online LiDAR suppression (Li et al., 15 Apr 2025)	F₁, PR, RR	9.7% F₁-score gain over SOTA baselines
QUBO suppression (Yamamura et al., 5 Feb 2025)	mAP, mAR	Up to +4.54 mAP / +9.89 mAR in crowded detection
DynORecon (Wang et al., 30 Sep 2024)	Volumetric error, FPS	~10 cm error, ~20 FPS, separate dynamic reconstr.

6. Practical Applications and Broader Implications

Effective dynamic object suppression is central to:

Construction of long-term, robust maps for urban autonomous vehicles, enabling localization free from transient (e.g., parked/moving cars, pedestrians) interference (Woo et al., 1 Jul 2024, Wu et al., 22 Jun 2024).
Real-time robot navigation and motion planning, where static-environment assumptions must be maintained for reliable collision avoidance (Li et al., 15 Apr 2025).
Large-scale image-based rendering, panorama generation, and multi-view scene understanding without spurious dynamic artifacts (Kanojia et al., 2019).
Improved object detection pipelines for dense/crowded visual scenes—critical in surveillance, retail analytics, and traffic/crowd monitoring—handling high density without degrading accuracy (Wang et al., 2020, Yamamura et al., 5 Feb 2025).
Autonomous system safety, via adversarial evaluation and fortification against dynamic suppression attacks (Hoory et al., 2020).

A plausible implication is that dynamic object suppression is increasingly integrated at multiple pipeline stages in complex systems, from low-level mapping to high-level semantic analysis and decision-making. Continuous advances in hybrid learning–geometry methods and quantum-ready optimization point toward sustained improvements in real-world, large-scale dynamic suppression.

7. Future Directions and Open Challenges

The literature points to several ongoing challenges and future research avenues:

Integration of more sophisticated temporal priors and motion modeling to handle semi-static, intermittently dynamic, or rare but impactful moving entities.
Adaptive thresholding and online, self-tuning suppression strategies that can generalize across modalities, sensor characteristics, or deployment environments without frequent retraining or calibration.
Cross-modality fusion suppressors (e.g., LiDAR+RGB+semantic data fusion) for more robust and general object suppression in 3D and appearance space, mitigating error propagation across sensor inputs (Li et al., 11 Jan 2025).
Exploration of quantum-accelerated optimization techniques to further improve the speed and effectiveness of set-based suppression in detection tasks (Yamamura et al., 5 Feb 2025).
Greater integration of dynamic object suppression into the end-to-end training of SLAM, detection, and segmentation networks for resilient real-world automation.

In summary, dynamic object suppression is a foundational technique across perception, mapping, and autonomous system domains, directly enabling accurate, artifact-free static reconstructions and robust performance in environments where motion is ubiquitous. Progress in this area continues to yield significant gains in computational efficiency, detection reliability, and robustness to environmental variability.