# zju3dv/InfiniDepth

[CVPR 2026] InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields

Repository: https://github.com/zju3dv/InfiniDepth
Canonical: https://ross.abutalabs.com/products/infinidepth
Homepage: https://zju3dv.github.io/InfiniDepth/
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2026-04-03T09:22:12+00:00

## Health v2 (maintenance only)
Score: 53/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 75, release rhythm 44, longevity 17
- inputs: {"age_days": 240, "days_push": 152, "days_rel": 162, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1073, forks 47 (observed 2026-08-28T04:03:28.734057+00:00)

## What it is
InfiniDepth is a CVPR 2026 research library for monocular depth estimation that represents depth as neural implicit fields, allowing depth queries at continuous 2D coordinates for arbitrary-resolution and fine-grained outputs. It also supports metric depth estimation with depth sensor input and single-image novel view synthesis via 3D Gaussian Splatting.

## Use cases
- estimate depth from a single RGB image at any output resolution
- generate 16K-resolution depth maps from photos
- recover fine-grained point clouds from one image
- estimate metric depth using RGB plus a depth sensor
- create 3D Gaussian Splatting scenes from a single view
- perform novel view synthesis with large viewpoint shifts
- evaluate depth models on high-resolution 4K benchmarks

## When to choose
- you need depth maps at resolutions beyond the input image grid
- you want fine geometric detail recovery from single images
- you need metric depth from RGB plus depth sensor data
- you want single-image 3DGS reconstruction or novel view synthesis

## When to avoid
- you need real-time depth on edge devices
- you want a simple plug-and-play depth estimator without GPU research setup
- you need multi-view or stereo depth estimation

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, graphics
- domain: computer-vision, machine-learning, deep-learning, graphics, artificial-intelligence
- platform: python, cross-platform
- tags: depth-estimation, monocular-depth, neural-implicit-fields, 3d-reconstruction, gaussian-splatting, novel-view-synthesis, metric-depth, cvpr-2026, gradio-demo, research-code, linux, gpu

## Member repositories
- zju3dv/InfiniDepth (main) score 53

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:28.734057+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:53:39.112577+00:00, confidence not recorded.
  - readme: https://github.com/zju3dv/InfiniDepth (fetched 2026-08-28T04:03:28.734057+00:00, sha 97b3354c23db)
  - homepage: https://zju3dv.github.io/InfiniDepth/ (fetched 2026-08-29T12:55:46.762399+00:00, sha 2b65a8307433)
- Data as of 2026-08-30T08:39:29.467469+00:00.