# InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

arXiv: https://arxiv.org/abs/2601.03252 · HTML: https://mlsanity.com/paper/2601.03252/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 53 references that are in the map.
- First posted on arXiv 6 Jan 2026.

## Builds on (most cited first, 30 of 44)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [DINOv3](https://mlsanity.com/paper/2508.10104/) (2025)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Deep Ordinal Regression Network for Monocular Depth Estimation](https://mlsanity.com/paper/1806.02446/) (2018)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2011.02523/) (2020)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [BlendedMVS: A Large-scale Dataset for Generalized Multi-view Stereo Networks](https://mlsanity.com/paper/1911.10127/) (2019)
- [Depth Pro: Sharp Monocular Metric Depth in Less Than a Second](https://mlsanity.com/paper/2410.02073/) (2024)
- [Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://mlsanity.com/paper/2404.15506/) (2024)
- [UniDepth: Universal Monocular Metric Depth Estimation](https://mlsanity.com/paper/2403.18913/) (2024)
- [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://mlsanity.com/paper/2307.10984/) (2023)
- [MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision](https://mlsanity.com/paper/2410.19115/) (2024)
- [DepthSplat: Connecting Gaussian Splatting and Depth](https://mlsanity.com/paper/2410.13862/) (2024)
- [DeepMVS: Learning Multi-view Stereopsis](https://mlsanity.com/paper/1804.00650/) (2018)
- [MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details](https://mlsanity.com/paper/2507.02546/) (2025)
- [DIODE: A Dense Indoor and Outdoor DEpth Dataset](https://mlsanity.com/paper/1908.00463/) (2019)
- [Learning to Recover 3D Scene Shape from a Single Image](https://mlsanity.com/paper/2012.09365/) (2020)
- [Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation](https://mlsanity.com/paper/2203.11483/) (2022)
- [MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond](https://mlsanity.com/paper/2309.16553/) (2023)
- [FoundationStereo: Zero-Shot Stereo Matching](https://mlsanity.com/paper/2501.09898/) (2025)
- [Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields](https://mlsanity.com/paper/1502.07411/) (2015)

## Built on by (most cited first, 23 of 23)

- [MoGe-3: Fine-Detail Monocular Geometry Estimation with Self-Guided Sparse Volumetric Refinement](https://mlsanity.com/paper/2607.17967/) (2026)
- [EVolSplat4D: Efficient Volume-based Gaussian Splatting for 4D Urban Scene Synthesis](https://mlsanity.com/paper/2601.15951/) (2026)
- [Scientific reasoning does not reliably translate into scientific forecasting in frontier AI](https://mlsanity.com/paper/2605.22681/) (2026)
- [AerialMetric: Benchmarking and Adapting UAV Monocular Metric Depth Estimation in the Real World](https://mlsanity.com/paper/2606.29716/) (2026)
- [SurGe: Improved Surface Geometry in Point Maps](https://mlsanity.com/paper/2605.31577/) (2026)
- [PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations](https://mlsanity.com/paper/2605.11594/) (2026)
- [IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation](https://mlsanity.com/paper/2605.16258/) (2026)
- [InfiniSplat: Implicit Gaussian Decoding for Large-Baseline Monocular View Synthesis](https://mlsanity.com/paper/2608.02437/) (2026)
- [PXDepth: Pixel-Space Modeling for Structure Preserving Monocular Depth Estimation](https://mlsanity.com/paper/2608.16984/) (2026)
- [UniQuery4R: Unified 4D Scene Reconstruction from a Single Query](https://mlsanity.com/paper/2608.17283/) (2026)
- [Marigold V2: Revisiting Diffusion Transformers for Monocular Depth Estimation](https://mlsanity.com/paper/2609.08084/) (2026)
- [Depth2Pose: A Pose-Based Benchmark for Monocular Depth Estimation without Ground-Truth Depth](https://mlsanity.com/paper/2605.19797/) (2026)
- [VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching](https://mlsanity.com/paper/2605.31466/) (2026)
- [HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection](https://mlsanity.com/paper/2606.31172/) (2026)
- [DrivingDepth: Sparse-Prompted Pixel-wise Scale Correction for Driving Depth Estimation](https://mlsanity.com/paper/2606.31488/) (2026)
- [Depth Estimators Are Implicit Neural Fields for 3D Scene Geometry Inpainting and Reconstruction](https://mlsanity.com/paper/2607.16286/) (2026)
- [RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark](https://mlsanity.com/paper/2608.30820/) (2026)
- [FoundDSR: A Generalizable Foundation Model with Guided 2D Gaussian Splatting for Depth Super-Resolution](https://mlsanity.com/paper/2609.32323/) (2026)
- [ARROW: Arbitrary Reconstruction and Tracking of 4D Observations in the Wild](https://mlsanity.com/paper/2610.01314/) (2026)
- [EagleDepth: Efficient Fine-Grained Depth Estimation via Pixel Diffusion Decoder](https://mlsanity.com/paper/2610.04554/) (2026)
- [Pumpire: Unified Benchmark for Metric Distance Estimation](https://mlsanity.com/paper/2610.12423/) (2026)
- [GeoBridge: Evolving Transferable Decoder Topologies for Geometric Vision](https://mlsanity.com/paper/2610.15280/) (2026)
- [The Midas Touch for Metric Depth: Interpretable Metricization for Generalizable Depth Perception](https://mlsanity.com/paper/2610.16749/) (2026)

## Abstract (arXiv)

Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decoder, we can query depth at continuous 2D coordinates, enabling arbitrary-resolution and fine-grained depth estimation. To better assess our method's capabilities, we curate a high-quality 4K synthetic benchmark from five different games, spanning diverse scenes with rich geometric and appearance details. Extensive experiments demonstrate that InfiniDepth achieves state-of-the-art performance on both synthetic and real-world benchmarks across relative and metric depth estimation tasks, particularly excelling in fine-detail regions. It also benefits the task of novel view synthesis under large viewpoint shifts, producing high-quality results with fewer holes and artifacts.