Interaction-Region Depth Estimation

Updated 2 January 2026

Interaction-region depth estimation is a specialized process that focuses on accurately predicting depth in zones where physical or semantic interactions occur.
It leverages ROI-guided deep networks, attention mechanisms, and multi-modal sensor fusion to enhance precision in challenging imaging scenarios.
Practical implementations benefit robotics and manipulation tasks by using adaptive loss functions and region-specific supervision to ensure high geometric fidelity.

Interaction-region depth estimation refers to the process of accurately measuring or predicting geometric depth specifically within spatial zones where contact, physical manipulation, or semantic interaction between agents (e.g., robots, humans) and the environment occurs. This problem class spans traditional and learning-based depth sensors, and encompasses methodologies for both localizing and resolving the geometry of hand–object, robot–object, or general agent–scene interaction zones at high precision, even in difficult imaging scenarios or using minimal data. The field has grown to include architectures that leverage deep networks, event-based vision, region-conditioned attention, stereo priors, and render–localize–lift paradigms for robust, region-focused geometric inference.

1. Problem Formulation and Taxonomy

Interaction-region depth estimation explicitly prioritizes depth accuracy within a limited pixel support, defined by application-driven region-of-interest (ROI) masks or zones inferred via semantic, physical, or task-related cues. This focus contrasts with generic, global depth estimation, and is motivated by manipulation tasks (e.g., grasping), 3D affordance reasoning, or non-contact interaction in robotics and embodied AI.

Key Taxonomies:

Region definition: Functional contact points (DbP (Goodrich et al., 2020)), grasp zones (latent loss weighting (Yasir et al., 17 Feb 2025)), semantic ROIs (ROIFormer (Xing et al., 2022)), or geometric overlaps across sensors (SDGE (Xu et al., 2024)).
Input modality: Single-view RGB, RGB-D, event streams (Active Event Alignment (Cai et al., 2024)), multi-view stereo, or joint sensor architectures.
Supervision signal: Physical interaction feedback (DbP), self-supervised photometric reprojection (ROIFormer, SDGE), stereo pseudo-labels (SDGE), or synthetic/real 3D contact annotations (InteractVLM (Dwivedi et al., 7 Apr 2025)).
Output granularity: Dense per-pixel depth maps, sparse (interaction-pixel) predictions, or 3D contact point clouds.

A notable trend is the growing use of attention and region-weighted losses, structured ROI proposals, and cross-modal priors to focus model capacity on maximizing geometric fidelity in interaction-relevant areas.

2. Model Architectures for Region-specific Depth

Recent architectures for interaction-region depth estimation share a focus on (a) encoding local geometric features with ROI sensitivity, (b) multi-task integration with semantic or segmentation cues, and (c) diverse mechanisms for infusing explicit region-level guidance into network inference.

Latent-Space Feature Guidance (Yasir et al., 17 Feb 2025):

Dual-stream encoder–decoder with RGB→depth and depth→depth pathways; per-region latent loss and gradient loss with ROI weighting concentrate error signals within the interaction region, enabling sharper boundaries and sub-millimeter precision in grasp zones.

Attention-Conditioned Transformers (Xing et al., 2022):

ROIFormer’s attention restricts computation to local, per-query, adaptively predicted regions, reducing irrelevant context and empirically accelerating convergence. Region bounding boxes are dynamically inferred from local semantics; M parallel attention heads and hierarchical pyramid placement allow cross-scale aggregation for fine boundary localization.

Event Camera Approaches (Cai et al., 2024):

The Active Event Alignment framework directly estimates object-wise or region-of-interest depth by optimizing for compensatory virtual camera rotations that null event motion, leveraging the inverse proportionality between required rotation and distance within each patch; segmentation masks define the interaction regions.

Self-supervised Interaction Learning (Goodrich et al., 2020):

The Depth by Poking pipeline treats each robot contact location as a sparse, high-certainty depth measurement, training a fully convolutional encoder–decoder to regress to physical z-coordinates only for interaction pixels. The architecture integrates aleatoric uncertainty heads and multi-modal fusion (RGB + depth cues), with losses dominated by the labeled interaction region.

Stereo-guided Priors and Fusion (Xu et al., 2024):

SDGE circumvents lack of global multi-view constraints by using stereo priors from overlap zones (interaction regions between adjacent cameras). These priors are injected as additional input channels or pseudo-supervision during depth prediction, substantially improving both overlap and cross-view consistency, even with limited overlap.

Render–Localize–Lift Pipelines (Dwivedi et al., 7 Apr 2025):

InteractVLM disentangles the estimation process into multi-view synthetic rendering, Vision-LLM (VLM)-driven ROI localization in 2D, and geometric lifting to 3D by precisely intersecting camera rays with canonical object meshes. Cross-attention injects VLM token embeddings as semantic conditioning, and losses are concentrated on rendered mask regions and contact point sets.

Steerable Depth Transformers (Min et al., 21 Nov 2025):

DepthFocus introduces direct control over the “focused” depth via an explicit conditioning parameter, routed through both mixture-of-experts and cross-attention modules, enabling selective estimation within ambiguous or multi-layered (transmissive) interaction regions.

3. Loss Design, Supervision and Region Focusing

Loss formulations are central to maximizing depth accuracy at interaction regions, often necessitating spatial reweighting or region mask guidance:

ROI-weighted Latent and Gradient Losses (Yasir et al., 17 Feb 2025): Applying higher loss weights inside ROI masks, coupled with gradient-level losses on features and images, is essential for preserving boundary integrity in grasp/contact estimation. Latent loss ensures the predicted and target feature maps at region-relevant layers match in structural content.
Semantic ROI Attention Losses (Xing et al., 2022): Fusion of photometric/edge-aware smoothness and segmentation cross-entropy, with attention focused inside dynamically detected ROIs, ensures selective representation learning.
Self-supervised/Physical Labels (Goodrich et al., 2020): Only contact-verified points are used as ground truth during training. Non-contact regions receive no loss, and auxiliary uncertainty-based penalties can be introduced.
Stereo Prior Consistency (Xu et al., 2024): Additional L1, virtual-normal, or ranking losses enforce that network predictions in the overlap region match stereo-derived depth, effectively acting as strong pseudo-labels in ROIs.
Behavioral/Alignment Maximization (Cai et al., 2024): Negative-Binomial likelihood or Poisson event-alignment maximization computes the “stabilization” quality per region, with physical alignment as the supervisory signal for regionwise depth ordering.
Multi-task Cross-Domain Fusion: ROI-pooling, auxiliary segmentation, or multi-head decoders are typical for increasing focus and representational power within interaction zones (Yasir et al., 17 Feb 2025, Dwivedi et al., 7 Apr 2025).

4. Empirical Evaluations and Quantitative Advances

Interaction-region depth methods significantly advance performance over prior art when evaluated on metrics specific to targeted zones or under physically realistic manipulation setups:

Method	Testbed / Region	RMSE (mm) ↓	AbsRel ↓	F1 (Contact) (%) ↑	Key Gains
Latent Space NN	NYU-Depth-v2 / grasp zones	0.416	–	–	–54.1% vs Eigen et al.
Depth by Poking	Bin objects / contact pxls	13.1	–	–	1/3 error of RealSense, ~20 mm on adversarial
ROIFormer	KITTI ROI / self-sup.	–	0.100	–	New SOTA at AbsRel, RMSE (4.336 m)
Active Event Align	EVIMO2 ROI regions	0.725	0.273	–	16% RMSE reduction, δ<1.25: 56.3%
SDGE	Panoramic overlaps	–	0.142†	–	3–5 pt AbsRel drop (ROI), ~25% cross-view gain
InteractVLM	DAMON contact mesh	–	–	75.6	+20–45% F1 over previous, Geo error 2.89 cm

†AbsRel for overlap (bundle-adjusted extrinsics) (Xu et al., 2024).

Performance improvements are driven by model and loss localization to ROI, real or synthetic region-level ground truth, and fusion of physical, semantic, or stereo priors.

5. Implementation Protocols and Practical Considerations

Implementation procedures typically involve the following:

Region Mask Construction: Masks are obtained via ground-truth interaction logs (Goodrich et al., 2020), segmentation branches (Yasir et al., 17 Feb 2025), or ROI predictors (Xing et al., 2022).
Data Augmentation: Spatial oversampling or crop biasing near interaction zones, e.g., handles/edges (Yasir et al., 17 Feb 2025), or sampling around contact pixels (Goodrich et al., 2020).
Architecture Variants: Multi-pathway encoders/decoders (duo streams, ROI pooling, lateral skip strategies), transformer-based ROI abstraction, and multi-scale/fusion heads for interaction density control.
Hardware: Designs favor real-time inference (e.g., Active Event Alignment at 20 Hz, ROIFormer at 33 fps), and moderate GPU requirements (~38 hours for 2M-sample training (Yasir et al., 17 Feb 2025)).
Supervision Mix: Hybrid pipelines allow for joint physical, synthetic, and pseudo-labeled supervision, with varying region-specific label densities.

6. Limitations, Failure Modes, and Future Directions

Limitations are tightly linked to the quality and alignment of regions:

Physical Constraint Violations: Forward/backward translation (z-motion) not accounted for in ROI-event alignment reduces depth fidelity (Cai et al., 2024).
Sparse Labeling: Interaction-grounded training is label-sparse, challenging for open-world generalization unless fused with synthetic data or auxiliary cues (Goodrich et al., 2020, Dwivedi et al., 7 Apr 2025).
Non-Rigid Dynamics: Fast or non-rigid ROIs complicate stabilization-based techniques and transfer learning.
Semantic/Geometric Ambiguity: DepthFocus and VLM-based models remain challenged by ambiguous object affordance or human-object overlaps without sufficient shape priors (Min et al., 21 Nov 2025, Dwivedi et al., 7 Apr 2025).
Open-Set and Cross-Domain: Cross-domain adaptation and sim-to-real transfer hinge on auxiliary losses (segmentation, data curation), and model robustness to occlusion and pose error.

Future work targets tight integration of language- and vision-based affordance cues, joint end-to-end learning of render–localize–lift, soft region confidence calibration, and the development of rich real-world 3D contact datasets (Dwivedi et al., 7 Apr 2025).

In sum, interaction-region depth estimation is structurally distinct from global scene depth prediction by virtue of its region prioritization, requirement for precise contact or affordance resolution, and frequent reliance on multi-modal, cross-domain, or behavior-guided supervision. Advances in latent-space encoding, adaptive ROI attention, physical interaction labeling, stereo-guided priors, and controllable transformer architectures have established new state-of-the-art fidelity within these regions, directly impacting robotics, manipulation, and embodied task performance (Yasir et al., 17 Feb 2025, Xing et al., 2022, Cai et al., 2024, Goodrich et al., 2020, Xu et al., 2024, Dwivedi et al., 7 Apr 2025, Min et al., 21 Nov 2025).