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AnyDepth: Depth Estimation Made Easy (2601.02760v1)
Published 6 Jan 2026 in cs.CV
Abstract: Monocular depth estimation aims to recover the depth information of 3D scenes from 2D images. Recent work has made significant progress, but its reliance on large-scale datasets and complex decoders has limited its efficiency and generalization ability. In this paper, we propose a lightweight and data-centric framework for zero-shot monocular depth estimation. We first adopt DINOv3 as the visual encoder to obtain high-quality dense features. Secondly, to address the inherent drawbacks of the complex structure of the DPT, we design the Simple Depth Transformer (SDT), a compact transformer-based decoder. Compared to the DPT, it uses a single-path feature fusion and upsampling process to reduce the computational overhead of cross-scale feature fusion, achieving higher accuracy while reducing the number of parameters by approximately 85%-89%. Furthermore, we propose a quality-based filtering strategy to filter out harmful samples, thereby reducing dataset size while improving overall training quality. Extensive experiments on five benchmarks demonstrate that our framework surpasses the DPT in accuracy. This work highlights the importance of balancing model design and data quality for achieving efficient and generalizable zero-shot depth estimation. Code: https://github.com/AIGeeksGroup/AnyDepth. Website: https://aigeeksgroup.github.io/AnyDepth.
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Explaining “AnyDepth: Depth Estimation Made Easy”
What is this paper about?
This paper is about teaching a computer to guess how far away things are in a picture using just one image (like seeing depth with one eye). This is called monocular depth estimation. The authors introduce a simple, fast system—called AnyDepth—that works well on many different kinds of scenes without needing to be retrained for each new situation (this “works anywhere” ability is called zero-shot).
What questions did the researchers ask?
The authors set out to answer three practical questions:
- Can a simpler design predict depth just as well as more complicated models?
- If we train with fewer but higher-quality examples, can we still get great results?
- Can the model be small and fast enough to run on everyday devices (like a small robot computer)?
How did they do it? (Methods in everyday language)
Think of the system as two main parts: a reader and a writer.
- The reader (encoder): They use a pre-trained vision model called DINOv3 to “read” the image and extract useful clues—like textures, edges, and shapes—without changing this part during training. It’s like asking a very experienced photographer to point out important details in a scene.
- The writer (decoder): They introduce a new, lightweight decoder called the Simple Depth Transformer (SDT). It turns those clues into a full depth map (a picture where each pixel shows how far away it is).
What makes SDT different and simpler:
- One path, not many: Older systems (like DPT) rebuild and mix features at multiple sizes using many branches, which is slow and heavy. SDT first combines information, then rebuilds the depth image in one clean path. Imagine mixing all your notes first and then writing a single clean summary, instead of juggling four versions at once.
- Smart detail booster (Spatial Detail Enhancer): A small module that sharpens fine details so edges (like table borders or chair legs) don’t look blurry.
- Smart upscaling (DySample): When making the depth image bigger, SDT doesn’t just stretch it (which blurs details). It uses a learnable “smart magnifier” that carefully samples pixels to keep edges crisp. It scales up gradually (by 2× several times) instead of jumping straight to full size, which is more stable and accurate.
Data matters too (filtering for quality):
- The team looked closely at the training data and removed low-quality examples that could confuse the model.
- Two simple checks guided the filtering:
- Depth Distribution Score: Does the image contain a good spread of distances (not just all near or all far)?
- Gradient Continuity Score: Do depth values change smoothly on flat surfaces and sharply at object boundaries (instead of being noisy)?
- They also removed images with too few valid depth pixels. After filtering, they trained on about 369,000 higher-quality images (much less than some other works that use tens of millions).
Training style:
- The encoder (reader) stays frozen.
- The decoder (writer) learns to predict relative depth (who is closer/farther), which is enough for many tasks.
- Images are trained at a fairly high resolution (768×768), and training only takes a few epochs because the design is efficient.
What did they find, and why does it matter?
Here are the main takeaways:
- Strong accuracy with a simpler design: AnyDepth matches or beats a popular baseline (DPT) on several test sets (indoor and outdoor) while being much leaner.
- Much smaller model: The new decoder (SDT) uses about 85–89% fewer parameters than DPT’s decoder, yet maintains or improves accuracy. Smaller models are easier to train, share, and run.
- Faster and more efficient: SDT cuts computation (FLOPs) by around 37% and is quicker at making predictions, especially at higher image sizes. On a small edge device (Jetson Orin Nano), it runs faster, uses less memory, and still produces clear results.
- Data quality beats data quantity: By filtering out noisy training samples, the model trained on far fewer images (about 369K) still performed very well. The ablation tests show that each piece—filtering, the detail enhancer, and the smart upscaler—adds clear improvements.
Why it matters:
- You don’t need massive datasets or huge models to get strong depth estimation. A smart design plus better data quality can be enough.
- This makes depth estimation more accessible for researchers, students, and engineers who don’t have giant computers or massive budgets.
- It also helps robotics and mobile apps, where speed and memory are limited.
What could this change in the future?
AnyDepth shows a practical path forward:
- Simpler, lighter models that are easier to reproduce and deploy.
- A shift in mindset: focus on cleaning and curating data, not just collecting more of it.
- Potential to extend the same ideas to related tasks, like predicting exact metric depth (distances in meters) or surface normals (which way a surface is facing).
In short, the paper proves that a clean, carefully designed system—with attention to data quality—can deliver strong depth estimation that runs fast, even on small devices.
Knowledge Gaps
Knowledge gaps, limitations, and open questions
Below is a concise list of concrete gaps and unresolved questions to guide future work:
- Metric depth: The model is trained/evaluated affine-invariant only; no experiments on absolute scale recovery, scale calibration, or metric depth accuracy across datasets (e.g., KITTI Eigen split).
- Evaluation metrics: Results report AbsRel and δ1 only; no δ2/δ3, RMSE/logRMSE, SILog, boundary/edge accuracy, or uncertainty calibration, limiting comparability with prior work.
- Real-world quantification: Real-world tests are qualitative and limited to three indoor scenarios; no quantitative ground-truth-based evaluation (e.g., RGB-D sensors), no dynamic scenes, adverse weather, motion blur, or low-light stress tests.
- Domain gap analysis: Training uses only synthetic data; the paper lacks a systematic study of synthetic→real domain shift, failure modes, and whether small-scale real fine-tuning or self-training closes the gap.
- Dataset filtering validity: The quality metrics (Depth Distribution and Gradient Continuity) assume access to reliable depth GT and may penalize valid high-frequency geometry (e.g., foliage); no sensitivity analysis to thresholds, no correlation with downstream accuracy, and no human-in-the-loop validation.
- Generality of filtering: The filtering requires GT depth and may not transfer to unlabeled/web-scale data curation; it is unclear how to adapt it for self-supervised or pseudo-label settings.
- Ablations on filtering policy: Only a single “filter bottom 20% per metric” policy is tried; no exploration of alternative weighting, joint scoring, per-dataset thresholds, or curriculum sampling strategies.
- Encoder freezing: The encoder is always frozen; missing analysis of partial or full unfreezing, layer-wise learning rates, or low-rank adaptation and their effect on accuracy and efficiency.
- Fusion design limits: SDT uses global per-layer scalar fusion weights; no exploration of image- or token-adaptive fusion (e.g., spatially varying weights, attention-based routing) or the risk of over-reliance on a single layer.
- Feature selection: The chosen layer indices ([2,5,8,11] etc.) are fixed; no ablation on which transformer layers to fuse, or whether more/fewer layers improve results/efficiency.
- Upsampler choice: DySample is adopted without comparison to other learnable upsamplers (e.g., CARAFE, pixel shuffle, deformable upsampling); no study of artifacts or robustness under aliasing.
- SDE design: The Spatial Detail Enhancer is minimal (DWConv+BN+ReLU); no comparisons to stronger edge/detail modules (e.g., guided filtering, Laplacian refinement, boundary losses) or learned edge-aware priors.
- Loss function scope: Only SSI and gradient losses are used; no exploration of cross-task priors (normals, edges), structural/planarity constraints, surface smoothness vs. edge preservation trade-offs, or uncertainty-aware losses.
- Hardware efficiency: Latency and memory are reported for H100 and Jetson Orin Nano, but not for CPUs/mobile NPUs; power consumption, thermal throttling, and quantization/pruning impacts are unreported.
- Real-time feasibility: Edge-device FPS remains low (≤1.2 FPS at 512×512); no optimization roadmap (quantization, mixed precision, kernel fusion), nor accuracy-vs-latency Pareto curves.
- High-resolution scaling: While 1024×1024 FLOPs/latency are reported, there is no accuracy evaluation at 2K/4K inputs or tiling strategies to preserve detail without excessive compute.
- Fairness/controls in SOTA comparisons: Comparisons mix different training data scales/recipes; a controlled study (same data, same encoder, different decoders) against leading decoders beyond DPT is missing.
- Downstream tasks: No evaluation of how AnyDepth improves downstream applications (3D reconstruction, control, SLAM), nor robustness to task-specific nuisances (reflective/transparent surfaces).
- Uncertainty estimation: The model provides no confidence maps or aleatoric/epistemic uncertainty; utility for safety-critical or planning systems is unclear.
- Failure mode analysis: The paper lacks systematic error analysis (thin structures, textureless regions, occlusion boundaries, reflective/transparent materials) and targeted remedies.
- Data scaling laws: Claims about “less but better” data are not backed by scaling curves vs. accuracy; no controlled study of dataset size/quality trade-offs across multiple quality thresholds.
- Training regimen: Only 5 epochs at 768×768 are used; no convergence analysis, longer schedules, or exploration of optimizer/scheduler choices and their effect on generalization.
- Intrinsics and FOV: No study of robustness to varying camera intrinsics, focal lengths, rolling shutter, or lens distortions common in real deployments.
- Robustness to perturbations: No evaluation under image corruptions (noise, JPEG, blur), lighting changes, or adversarial perturbations; it is unknown how SDT handles distribution shifts.
- Interpretability: No inspection of learned fusion weights, attention maps, or feature attributions to understand what SDT leverages at different layers and why.
- Reassembly strategy theory: The paper motivates fusion-before-reassemble empirically; a theoretical or analytical justification (e.g., information preservation, error propagation) is missing.
- Licensing and deployment: DySample and DINOv3 dependencies’ licenses and on-device portability constraints (e.g., custom CUDA ops) are not discussed for practical adoption.
Glossary
- AbsRel: Absolute mean relative error used to evaluate depth prediction accuracy; "We use the absolute mean relative error(AbsRel)"
- AdamW: Optimizer with decoupled weight decay commonly used in training deep models; "We use AdamW with a base learning rate of 1 x 10-3"
- Affine-invariant depth: Depth representation invariant to scale and translation, focusing on relative structure; "learn to predict affine-invariant depth"
- Batch normalization: Normalization technique applied to stabilize and accelerate training; "followed by batch normalization."
- Bilinear interpolation: Fixed, non-learnable upsampling method that can blur high-frequency details; "DPT uses fixed bilinear interpolation for upsampling"
- Class token: Special transformer token summarizing global information used alongside spatial tokens; "For the class token, we keep the same processing as DPT"
- Depth Distribution Score: Metric assessing how uniformly depth values are distributed across the range; "we propose a Depth Distribution Score"
- Depthwise convolution: Convolution that operates independently on each channel to model local spatial details efficiently; "Depthwise convolution for local spatial modeling"
- Differentiable grid sampling: Operation enabling gradient-based resampling at learned offsets during upsampling; "then uses differentiable grid sampling to resample to high-resolution features."
- DINOv3: Self-supervised visual transformer backbone used for high-quality dense features; "We first adopt DINOv3 as the visual encoder"
- Disparity: Inverse of depth used as a regression target to stabilize training; "predict disparity d' = 1/d"
- DySample: Learnable upsampling module that constructs offset sampling grids adaptively; "we use DySample (Liu et al., 2023) as the upsampler"
- Feature Pyramid Networks (FPN): Multi-scale architecture that merges semantic and low-level features top-down; "FPN (Lin et al., 2017) proposes a top-down architecture"
- Fusion-reassemble strategy: Approach that fuses tokens before spatial reassembly to avoid cross-scale overhead; "In contrast, SDT employs a fusion- reassemble strategy"
- GELU: Gaussian Error Linear Unit activation function used in transformer decoders; "followed by a GELU non-linearity"
- Gradient Continuity Score: Metric evaluating smoothness of depth gradients to detect noisy samples; "Gradient Continuity Score (higher is better)"
- Gradient matching loss: Objective encouraging predicted depth gradients to match ground-truth gradients; "a gradient matching loss Lgm"
- Learnable dynamic sampler: Trainable upsampling mechanism that adapts sampling positions from low-res features; "adopt a learnable dynamic sampler (Eq. 6)."
- PolyLR scheduler: Polynomial learning rate decay schedule controlling optimization over training; "a PolyLR scheduler with power 0.9"
- Residual connection: Skip connection adding input features to transformed features to aid optimization; "via a residual connection"
- Reassemble-fusion strategy: Decoder design that first maps tokens to multi-scale feature maps and then fuses them; "DPT employs a reassemble-fusion strategy."
- Scale- and shift-invariant loss: Training objective robust to dataset-specific depth scaling and offsets; "use a scale- and shift-invariant loss Essi"
- SDT (Simple Depth Transformer): Lightweight single-path transformer decoder for depth reconstruction; "we design the Simple Depth Transformer (SDT), a compact transformer-based decoder."
- Spatial Detail Enhancer (SDE): Module refining reshaped feature maps to recover local texture details; "The Spatial Detail Enhancer (SDE) module ensures finer- grained predictions."
- Vision Transformer (ViT): Transformer-based image encoder producing high-resolution features; "DPT utilizes the ViT (Dosovitskiy et al., 2020) backbone network"
- Zero-shot monocular depth estimation: Predicting depth from a single image without task-specific fine-tuning; "zero-shot monocular depth estimation"
Practical Applications
Immediate Applications
Below is a concise set of practical use cases that can be deployed now, leveraging AnyDepth’s zero-shot, lightweight depth estimation framework, SDT decoder, learnable upsampling, and data-centric filtering strategy.
- Edge robotics navigation and obstacle awareness (Robotics)
- Use case: Real-time obstacle detection, corridor following, and simple mapping on low-power platforms (e.g., Jetson Orin Nano), as demonstrated in the paper’s real-world evaluation.
- Tools/workflow: AnyDepth model as a ROS node; depth frames feeding local planners or SLAM front-ends for geometric cues.
- Assumptions/dependencies: Produces affine-invariant (relative) depth; for absolute distance, add scale-shift calibration (e.g., ground plane or known-size object), and ensure sufficient on-device compute or NPU/GPU.
- AR/VR occlusion, depth-aware effects, and mobile camera enhancements (Software, Consumer Tech)
- Use case: Improved bokeh, portrait relighting, background replacement, and realistic occlusion in AR apps using fast, edge-friendly depth from single RGB images.
- Tools/workflow: Integrate AnyDepth into mobile pipelines; expose depth to AR frameworks or image processing stacks; depth-aware shaders.
- Assumptions/dependencies: Relative depth suffices for visual effects; per-device performance will vary; for metric occlusion in industrial AR, add simple scale calibration.
- Creative production and 2.5D parallax from single images (Media, Entertainment)
- Use case: Depth-guided parallax animation, compositing, and scene relighting for editors; rapid single-image “depth matte” generation.
- Tools/workflow: Plug AnyDepth into Nuke/After Effects/Blender pipelines; export depth maps to stabilize ControlNet depth conditioning in diffusion workflows.
- Assumptions/dependencies: Image-domain generalization is strong but not perfect; for physically accurate relighting, metric calibration or multi-view data is needed.
- Depth-guided image generation and 3D content bootstrapping (Software, 3D)
- Use case: Use AnyDepth’s relative depth to condition diffusion/NeRF/3D Gaussian Splatting pipelines for better geometry priors and faster convergence in few-shot settings.
- Tools/workflow: Depth-to-Condition pipelines with Stable Diffusion + ControlNet; initialize NeRF/3DGS with depth priors for geometry regularization.
- Assumptions/dependencies: Relative depth aids structure, not scale; add simple post-hoc scale/shift estimation for metric tasks.
- Low-latency analytics for monocular video (Security, Retail, Smart Facilities)
- Use case: Rank-ordering distances to infer crowd flow, queue length, or foreground/background separation when stereo/LiDAR is unavailable.
- Tools/workflow: Video analytics engine ingesting AnyDepth depth maps to improve segmentation and tracking robustness.
- Assumptions/dependencies: Relative depth enables ordering but not precise range; camera intrinsics and scene constraints help derive scale if required.
- Academic reproducibility and teaching (Academia, Education)
- Use case: Classroom and research demos of modern depth estimation without large compute; reproducible baselines for zero-shot monocular depth.
- Tools/workflow: Use the open-source code to run labs, compare DPT vs. SDT, and teach data-centric curation.
- Assumptions/dependencies: GPU recommended for interactive demos; leverage provided training/evaluation scripts.
- Data-centric dataset QA for dense prediction tasks (Software, ML Ops)
- Use case: Filter harmful samples from synthetic or mixed datasets using the proposed Depth Distribution Score and Gradient Continuity Score to boost training signal and cut cost.
- Tools/workflow: Integrate the metrics into data ingestion pipelines; auto-flag samples with extreme concentration or gradient noise.
- Assumptions/dependencies: Metrics tuned for depth maps; thresholds may need adjustment per domain; extend cautiously to real-world datasets with different noise profiles.
- Faster, cheaper depth inference in legacy pipelines (Software)
- Use case: Drop-in replacement of DPT-style decoders with SDT to reduce parameters (≈85–89%), FLOPs (≈37%), memory, and latency at high resolutions.
- Tools/workflow: Swap decoder heads in existing ViT-based depth stacks; SDT + DySample for improved edges and fine details.
- Assumptions/dependencies: Maintain compatibility with backbone feature taps (e.g., DINOv3 layers); check licenses and model export to ONNX/TensorRT for deployment.
Long-Term Applications
These opportunities build on AnyDepth’s methods but require further research, scaling, calibration, or ecosystem development before broad deployment.
- Metric-depth monocular pipelines at scale (Robotics, AR/VR, Construction)
- Use case: True distance estimation from one camera for navigation, measurement, and industrial AR.
- Tools/workflow: Fuse AnyDepth with minimal auxiliary signals (IMU, ground-plane priors, camera intrinsics) or fine-tune on metric labels; add scale-shift estimators or learnable calibrators.
- Assumptions/dependencies: Requires calibration data or multi-task training; evaluate robustness across lenses, FOVs, and lighting conditions.
- Unified lightweight decoders for multi-task dense perception (Software, Robotics)
- Use case: Extend SDT to normals, albedo, edges, and semantics, delivering a single efficient head for multiple dense tasks.
- Tools/workflow: Multi-head SDT variants; shared encoder with task-specific heads and dynamic upsampling.
- Assumptions/dependencies: Need joint datasets and losses; validate cross-task interference and deployment resource budgets.
- Edge-first depth standardization across mass robotics and drones (Robotics)
- Use case: Standard depth service on commodity robots/drones for obstacle ranking, landing site assessment, and route planning without expensive sensors.
- Tools/workflow: “Depth-as-a-Service” microservices running AnyDepth; edge-accelerated builds (TensorRT, OpenVINO, CoreML).
- Assumptions/dependencies: Platform-specific acceleration; safety-critical deployments require redundancy (e.g., stereo or ultrasonic backup).
- 3D scene generation and editing with depth priors (Media, Gaming, Virtual Production)
- Use case: Production-grade pipelines where AnyDepth initial geometry improves 3D video generation (e.g., Gaussian splatting, feed-forward 3DGS) and accelerates scene editing.
- Tools/workflow: Integrate with 3DGS toolchains (e.g., voxel-aligned prediction, bottleneck-aware compression) and diffusion-based video synthesis.
- Assumptions/dependencies: Scale, texture realism, and temporal consistency need tuning; licensing and IP for pre-trained backbones must be respected.
- Healthcare and telemedicine visual measurement (Healthcare)
- Use case: Remote wound assessment, posture/ergonomics analysis, and home monitoring via depth cues when dedicated sensors are impractical.
- Tools/workflow: Depth-enhanced clinical apps with camera intrinsics calibration; integrate with pose estimation and medical-grade software.
- Assumptions/dependencies: Requires metric accuracy, clinical validation, and regulatory compliance (HIPAA, GDPR); robust performance across skin tones and lighting.
- Smart city analytics and policy-informed infrastructure (Public Sector, Policy)
- Use case: Deploy energy-efficient monocular analytics for pedestrian flow, occupancy, and safety in camera networks; encourage procurement of low-FLOPs models to reduce operational carbon.
- Tools/workflow: City-scale VMS integrating AnyDepth; policy frameworks favoring data quality metrics and lightweight models (e.g., dataset QA standards).
- Assumptions/dependencies: Privacy safeguards; domain evaluation across diverse environments; governance for synthetic data use and bias mitigation.
- Automated, generalizable data-quality scoring frameworks (Software, ML Ops)
- Use case: Evolve the paper’s depth-specific metrics into a broader, modality-agnostic data-centric QA suite for dense prediction datasets (segmentation, flow, disparity).
- Tools/workflow: Pluggable scoring services in data lakes; quality dashboards guiding curation and active learning.
- Assumptions/dependencies: Task-specific metric adaptation, empirical validation across modalities, and integration with labeling tools.
- Hardware-software co-design for learnable upsampling (Semiconductors, Software)
- Use case: Co-optimize DySample-like modules with modern NPUs/ISPs for high-fidelity upsampling in cameras and embedded platforms.
- Tools/workflow: Firmware updates enabling dynamic sampling grids; SDKs exposing learnable upsampling APIs to app developers.
- Assumptions/dependencies: Vendor support and silicon capabilities; real-time guarantees and power budgets.
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