Ground-Aware Monocular Perception

• The topic introduces methods that incorporate explicit ground-plane constraints into monocular 3D perception to improve depth, scale, and metric accuracy.
• It leverages geometric modeling, camera calibration, and plane equations to enhance object detection, SLAM, and free-space estimation across diverse scenarios.
• Applications in robotics, autonomous driving, and indoor navigation demonstrate robust performance with reduced ambiguity and efficient real-time processing.

Ground-aware monocular perception is the class of algorithms and architectures that explicitly incorporate ground-plane or floor-surface priors into single-camera (monocular) computer vision models, targeting accurate 3D reasoning in robotics, autonomous driving, mobile perception, and related areas. By leveraging explicit geometric constraints from the encountered ground, these methods alleviate fundamental ambiguites in monocular 3D perception: scale, depth, and metric meaning are intrinsically ambiguous from a single image unless environmental priors are injected. The emergence of ground-aware methods has driven advances across object detection, geometric mapping, localization, pose estimation, and traversability assessment, especially in resource- and sensor-constrained domains.

1. Core Ground-Plane Modeling Techniques

Modern ground-aware monocular perception frameworks consistently utilize the representation of the ground as a 3D plane, instantiated by various parameterizations:

• Explicit ground-plane equation: $aX + bY + cZ + d = 0$ in the camera frame, with (a,b,c) as the (unnormalized) plane normal, and d the signed distance (Yang et al., 2023).
• Camera height and orientation: Known extrinsic calibration or online estimation of height and roll/pitch is often pivotal (Zhou et al., 2023, Zhou et al., 2023).
• Canonical forms: In driving, the ground plane Y = EL (where EL is the known camera height) is commonly assumed (Liu et al., 2021).
• Locally planar or piecewise planar ground: Some approaches segment or adapt the ground model to local surface undulations (Elazab et al., 3 Dec 2025, Vosshans et al., 9 Jul 2025).

A representative mathematical machinery for back-projecting an image point (u,v) to a 3D ground location (X, Y, Z) relies on:

• The camera intrinsic matrix $K$
• Camera extrinsics (R, t) or real-time pose
• Imposing the ground-constraint (e.g., setting Z=0 for a flat horizontal ground) to solve for the metric placement of the pixel (Melo et al., 2022, Liu et al., 2021).

In stochastic or uncertain contexts, the covariance in plane parameters is explicitly propagated from measurement noise (Clement et al., 2017).

2. Algorithmic Integration across Perception Tasks

3D Object Detection

Ground-aware monocular 3D object detectors exploit ground priors to resolve depth ambiguities, refine anchor selection, and restrict localization hypotheses:

• Anchor filtering and adjustment: Anchors inconsistent with the ground constraint (e.g., centers that “float” or “sink” too far from ground) are excluded, enabling fine-grained screening and direct metric positioning from single images (Liu et al., 2021).
• Ground-aware feature fusion: Ground depth maps or plane-parameter maps are fused into deep features via explicit (convolutional (Liu et al., 2021), transformer-based (Zhou et al., 2023), or attention-based (Yang et al., 2023)) architectures.
• Bottom-center estimation: The pixel of ground contact is leveraged to back-project the object’s supporting point for metric pose estimation (Melo et al., 2022).

Depth, Road-Topography, and Free-Space Estimation

State-of-the-art self-supervised monocular depth approaches now directly estimate road-relative 3D structure by:

• Learning planar (ground) + deviation (γ) representations: e.g., each pixel’s height above the ground as a dimensionless parallax ratio (Elazab et al., 3 Dec 2025).
• Stixel-World intermediate representations: The scene is factored into column-wise thin “sticks” anchored on the ground, enabling lossy, bandwidth-efficient 3D, and freespace inference (Vosshans et al., 9 Jul 2025, Vosshans et al., 11 Jul 2024).

Odometry, SLAM, and Localization

Ground constraints anchor scale in monocular visual-inertial odometry (VIO), reducing drift and enabling robust metric pose estimation by:

• Explicitly modeling the camera-ground transformation (height, roll/pitch) in the optimization backbone (Zhou et al., 2023).
• Online calibration and bundle adjustment with plane-factor constraints for drift-free metric positioning (Clement et al., 2017, Zhou et al., 2023).

Obstacle and Traversability Discovery

In unique domains such as indoor mobile robots on reflective floors, ground-awareness is used to:

• Disambiguate real-world obstacles from mirror images based on geometry-induced parallax (Xue et al., 2 Jan 2024).
• Fuse ground-induced geometric cues with appearance via regressor or forest-based architectures.

3. Representative Architectures and Training Strategies

Table: Ground-Aware Architectural Strategies in Key Applications

Category	Main Approach (Example)	Ground Integration Mode
3D Object Detection	MoGDE (Zhou et al., 2023), MonoGAE (Yang et al., 2023)	Fusion of ground-depth/prior features into transformer or attention pipelines; pixelwise plane equations; dynamic anchor filtering
Monocular Depth/Road	Gamma-from-Mono (Elazab et al., 3 Dec 2025), StixelNExT++ (Vosshans et al., 9 Jul 2025)	Plane + deviation (γ) maps; stixelized regression from image; self-supervision with photometric and homography alignment
Odometry, SLAM	Ground-VIO (Zhou et al., 2023), VT&R (Clement et al., 2017)	Online estimation/calibration of camera-ground geometry, IPM-based ground feature tracking, factor-graph with plane constraints
Obstacle Discovery	ORG (Xue et al., 2 Jan 2024)	Ego-motion aided ground plane detection, appearance-geometry fusion

Architectural advances include:

• Ground-aware convolutional modules: Feature volumes are shifted and fused at the projected contact-point location determined by the ground constraint (Liu et al., 2021).
• Transformer/multi-head attention with cross-modal fusion: Ground-depth or plane parameters form a dense input to a decoder that discovers object support pixels (Zhou et al., 2023, Yang et al., 2023).
• Online or pretrained extrinsic estimation: Models either assume fixed geometric priors (e.g., road flatness with fixed mounting height) or regress camera-ground parameters at runtime (Zhou et al., 2023, Clement et al., 2017).
• LiDAR-based supervision for monocular models: Stixel pipelines and multi-layer scene representations are trained using automatically generated LiDAR ground-truth, enabling efficient downstream adaptation (Vosshans et al., 9 Jul 2025, Vosshans et al., 11 Jul 2024).

4. Application Domains and Empirical Performance

Ground-aware monocular perception underpins a broad range of applications:

• Urban autonomy and collective perception: Object-level 3D detection and semantic stixel representation, with state-of-the-art recall and precision over 30 m in real-time (10–100 ms/frame), are directly achieved from a single forward camera (Vosshans et al., 9 Jul 2025, Yang et al., 2023).
• Embedded, resource-constrained robotics: Real-time ball, robot, and goal localization for soccer robots matches or outperforms multi-view vision, with sub-centimeter accuracy within 1 m operating range purely via geometry (Melo et al., 2022).
• Visual-inertial odometry: Scale-drift is kept under 1% for urban/highway environments with a monocular + IMU setup, outperforming stereo baselines in certain regimes (Zhou et al., 2023).
• Indoor reflective floor scenarios: Misclassification rates of mirrored obstacles are reduced by >27 pp in pixel-TPR and 15 pp in instance-TPR, with strong generalization in motion-blur and odometry-noise stress tests (Xue et al., 2 Jan 2024).
• Human-object motion capture: By enforcing physically plausible ground contact under gravity, global translation and metric scale accuracy surpass pure kinematic monocular baselines (MPE 224 mm vs. 262 mm for VNect) (Dabral et al., 2021).

5. Robustness, Limitations, and Extensions

Explicit ground modeling generally improves robustness to scene, camera, and motion perturbations:

• Camera pose divergence: Depth-map-based pipelines are sensitive to pitch/roll noise, while regressing ground plane equations demonstrates greater resilience (e.g., moderate 3D AP drop under pose jitter: 67.8%→55% via global plane map, only 62% for refined plane map (Yang et al., 2023)).
• Domain adaptation: Representation leveraging scale-free ground-aware quantities (e.g., γ in (Elazab et al., 3 Dec 2025)) maintains performance across camera intrinsics and environments, avoiding manual calibration.
• Challenging geometries: Piecewise or locally planar modeling (common in stixel pipelines) yields better fit for varying terrains or non-flat roads (Vosshans et al., 9 Jul 2025, Elazab et al., 3 Dec 2025).
• Failure modes: Critical limitations arise when the ground assumption is violated, e.g., severe field curvature, unmodeled camera drift, occlusions at object contact, or dominant non-road planes. Errors in ground or horizon estimation directly propagate to downstream 3D metrics (Melo et al., 2022, Zhou et al., 2023).
• Extensions: Multi-plane environments (walls, curbs), online extrinsic re-calibration, fusion with IMU/odometry for moving platforms, and end-to-end 3D keypoint detectors removing the z=0 constraint are considered promising directions (Melo et al., 2022, Zhou et al., 2023).

6. Dataset Construction, Supervision, and Benchmarks

Many ground-aware approaches leverage automatic ground-truth generation from LiDAR or known field layouts:

• LiDAR-projected stixel ground-truth: Two-stage RANSAC/DBSCAN pipelines label vertical columns as ground/obstacle for monocular network training without manual annotation (Vosshans et al., 9 Jul 2025, Vosshans et al., 11 Jul 2024).
• Custom benchmarks: The ORG dataset for obstacle-on-reflective-ground 3D segmentation (Xue et al., 2 Jan 2024), and new stixel datasets for urban/free-space detection (Vosshans et al., 11 Jul 2024, Vosshans et al., 9 Jul 2025).
• KITTI, Waymo, Rope3D, DAIR-V2X-I: Widely adopted in large-scale monocular 3D detection, depth, and collective perception, with ground annotations and challenging real-world scenarios (Vosshans et al., 9 Jul 2025, Yang et al., 2023).
• Human-object interaction: Motion capture sequences with contact and ballistic episodes, camera parameters, and 2D/3D joint and object tracks for evaluating gravity- and ground-aware estimation quality (Dabral et al., 2021).

7. Broader Impact and Future Research Directions

The explicit fusion of ground geometry into monocular vision shifts core computer vision tasks from purely statistical inference toward physically interpretable, constraint-based modeling. Major implications include:

• Low-cost scalable 3D: Replaces the need for external depth sensors in common robotics and automotive scenarios.
• Collective perception and V2X: Sparse, ground-anchored stixel representations are more bandwidth- and computation-efficient for infrastructure-to-vehicle fusion (Vosshans et al., 9 Jul 2025).
• Improved robustness: Strong resilience to scene and pose shifts and realistic field/terrain imperfections.
• Self-supervised and transfer learning: Structural ground-aware cues support model adaptation and learning in the absence of dense labels (Elazab et al., 3 Dec 2025, Vosshans et al., 11 Jul 2024).
• Remaining challenges: Generalization to highly structured but non-flat terrain, real-time 3D under tight compute/power budgets, and development of unified, semantically-rich ground-aware 3D representations across all perception modalities.

As ground-aware monocular perception matures, further cross-pollination with SLAM, multimodal sensor fusion, and physically-driven neural architectures is anticipated to drive the next generation of robust, interpretable, and deployable 3D perception systems.