Record3D VS cppq

• 3D Scanning Capability
  Record3D allows users to capture 3D models using the iPhone's LiDAR sensor, providing a relatively accessible way to create detailed 3D scans.
• Easy Sharing
  Users can easily share their 3D recordings with others through a variety of formats, making it convenient for collaboration and showcasing work.
• Real-time Depth Viewing
  The app provides real-time depth information, allowing users to see the 3D layout as they record, which enhances the scanning experience.
• User-Friendly Interface
  Record3D offers a clean and user-friendly interface that simplifies the process of recording and managing 3D content.

Possible disadvantages

• Device Limitations
  The app requires specific hardware, such as iPhones with LiDAR sensors, restricting its usability to users with compatible devices.
• Processing Time
  Rendering and processing 3D models can take a significant amount of time, which might be inconvenient for users needing quick results.
• Quality Variability
  The quality of 3D scans can vary depending on the lighting conditions and complexity of the objects, which may not meet professional-grade expectations.
• File Size
  3D recordings can result in large file sizes, potentially creating storage issues for users with limited space on their devices.

• Lightweight C++ implementation
  cppq is written in modern C++ and designed to be a minimal, efficient task queue library, making it a good fit for performance-sensitive applications where the overhead of scripting language runtimes is undesirable.
• Redis-backed reliability
  Built on top of Redis as the message broker, cppq benefits from Redis's persistence, atomic operations, and wide adoption, giving the queue system durability and the ability to survive process restarts.
• Inspired by proven design (asynq)
  The library's architecture mirrors the popular Go asynq task queue, so it inherits a well-thought-out API design for task enqueueing, retries, and worker processing patterns that have already been validated in production systems.
• Native integration for C++ projects
  For teams already working in a C++ codebase, cppq allows adding background job processing without needing to introduce a separate service written in another language (like Go or Python), simplifying the technology stack.
• Open source and inspectable
  Being open source on GitHub, developers can review the source code, modify it for custom needs, and contribute improvements, which offers transparency not available with closed-source queueing solutions.

Possible disadvantages

• Limited maturity and battle-testing
  Compared to established task queue libraries like Sidekiq, Celery, or asynq itself, cppq is a newer and smaller project with less real-world production usage, which may translate into undiscovered bugs or edge cases.
• Smaller community and ecosystem
  Because it has far fewer users and contributors than mainstream task queue systems, finding community support, third-party tutorials, or pre-built integrations is more difficult.
• Documentation gaps
  As a smaller project, cppq may have less comprehensive documentation compared to major task queue frameworks, requiring developers to read source code directly to understand advanced features or edge-case behaviors.
• C++ build and dependency complexity
  Using cppq requires managing C++ build toolchains and dependencies (such as a Redis client library), which can be more cumbersome than installing a package via pip or npm in other ecosystems.
• Fewer built-in features and tooling
  cppq likely lacks the extensive ecosystem tooling of more mature queues, such as web-based monitoring dashboards, extensive metrics/observability integrations, or advanced scheduling features found in more established solutions.

No analysis of Record3D yet.

• [R] CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory + Code + Robot demo

  Best part, I believe, is that you should be able to train your own CLIP-Field for your living room if you have an hour, a decent GPU, and a way to get RGB-D video (an iPhone 13 Pro works great!) I hope you can give the code a try:... Source: about 4 years ago

• An End-to-End Guide to Photogrammetry with Mobile Devices

  If you don't need publication-quality 3D models, the latest iPhones have built-in LIDAR sensors, both front (for facial recognition) and back (for AR). There are a variety of apps that can use these built-in sensors to build 3D models of... - Source: Hacker News / about 5 years ago

• Show HN: Object Detection in an Hour

  Maybe worth to support front camera (TrueDepth camera) as well? Record3d gives pretty good accuracy (https://record3d.app/). I know probably not the best way to scan something without seeing the screen but better that than nothing. As a... - Source: Hacker News / about 5 years ago

Tracking cppq since Nov 2022.