# darglein/ADOP

Repository: https://github.com/darglein/ADOP
Canonical: https://ross.abutalabs.com/products/adop
Language: C++
License: MIT
License Family: permissive
Last push: 2024-02-02T12:59:09+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1785, "days_push": 943, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2026, forks 192 (observed 2026-08-28T04:06:06.843632+00:00)

## What it is
ADOP is a point-based differentiable neural rendering pipeline for scene refinement and novel view synthesis, implemented in C++/CUDA with PyTorch. It renders large point clouds in real time and jointly optimizes camera parameters, point positions/colors, and neural shading networks end-to-end.

## Use cases
- novel view synthesis from photos
- refine a point cloud reconstruction with a differentiable renderer
- render 100M+ point scenes in real time
- optimize camera poses and intrinsics jointly with rendering
- reproduce the ADOP paper from ACM TOG 2022

## When to choose
- you have an initial point cloud plus camera estimates and want photorealistic novel views
- you need a differentiable point-based renderer for research
- you want real-time rendering of very large point clouds

## When to avoid
- you want a maintained production library rather than research code
- you need a plug-and-play NeRF/Gaussian splatting tool with broad community support
- you lack a CUDA-capable GPU

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, graphics, image-processing, gpu-computing
- domain: computer-vision, graphics, deep-learning, machine-learning
- platform: cpp, python
- tags: neural-rendering, novel-view-synthesis, differentiable-rendering, point-cloud, radiance-fields, computer-graphics-research, linux, gpu, cuda

## Member repositories
- darglein/ADOP (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:06.843632+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 2026-08-30T02:59:43.871460+00:00, confidence not recorded.
  - readme: https://github.com/darglein/ADOP (fetched 2026-08-28T04:06:06.843632+00:00, sha 9e17579ea372)
- Data as of 2026-08-30T08:39:29.467469+00:00.