Apple's "Cubify Anything" introduces a new approach to 3D object detection within indoor scenes using monocular RGB images. It leverages a pre-trained 2D object detector to identify objects and then fits a cuboid to each detected object by estimating its 3D pose and dimensions. This method, dubbed "cubification," efficiently generates dense 3D models of indoor environments, suitable for applications like augmented reality and scene understanding. The approach simplifies the 3D detection pipeline by directly predicting cuboids instead of complex meshes or point clouds, enabling real-time performance on mobile devices. Importantly, Cubify Anything is designed to work on diverse indoor scenes without requiring specific training data for each scene.
This blog post by David Weisberg traces the evolution of Computer-Aided Design (CAD). Beginning with early sketchpad systems in the 1960s like Sutherland's Sketchpad, it highlights the development of foundational geometric modeling techniques and the emergence of companies like Dassault Systèmes (CATIA) and SDRC (IDEAS). The post then follows CAD's progression through the rise of parametric and solid modeling in the 1980s and 90s, facilitated by companies like Autodesk (AutoCAD) and PTC (Pro/ENGINEER). Finally, it touches on more recent advancements like direct modeling, cloud-based CAD, and the increasing accessibility of CAD software, culminating in modern tools like Shapr3D.
Hacker News users discussed the surprising longevity of some early CAD systems, with one commenter pointing out that CATIA, dating back to the late 1970s, is still heavily used in aerospace and automotive design. Others shared anecdotal experiences and historical details, including the evolution of CAD software interfaces (from text-based to graphical), the influence of different hardware platforms, and the challenges of data exchange between systems. Several commenters also mentioned open-source CAD alternatives like FreeCAD and OpenSCAD, noting their growing capabilities but acknowledging their limitations compared to established commercial products. The overall sentiment reflects an appreciation for the progress of CAD technology while recognizing the enduring relevance of some older systems.
Vincent Woo created an interactive 3D model of San Francisco's Sutro Tower using the Gaussian Splatting technique. This allows users to virtually explore the intricate structure of the tower with impressive detail and smooth performance in a web browser. The model is based on a real-world point cloud captured with lidar, offering a realistic and immersive experience of this iconic landmark.
Hacker News users generally praised the Sutro Tower 3D model, calling it "amazing," "very cool," and "impressive." Several commenters appreciated the technical aspects, noting the clever use of Gaussian Splats and the smooth performance even on mobile devices. Some discussed the model's size and loading time, with one suggesting potential optimizations like level-of-detail rendering. Others compared it to other 3D capture techniques like photogrammetry, pointing out the differences in visual style and data requirements. A few commenters also shared personal anecdotes about Sutro Tower, reflecting on its iconic presence in San Francisco.
Augurs is a demo showcasing a decentralized prediction market platform built on the Solana blockchain. It allows users to create and participate in prediction markets on various topics, using play money. The platform demonstrates features like creating binary (yes/no) markets, buying and selling shares representing outcomes, and visualizing probability distributions based on market activity. It aims to highlight the potential of decentralized prediction markets for aggregating information and forecasting future events in a transparent and trustless manner.
HN users discussed Augurs' demo, with several expressing skepticism about the claimed accuracy and generalizability of the model. Some questioned the choice of examples, suggesting they were cherry-picked and lacked complexity. Others pointed out potential biases in the training data and the inherent difficulty of accurately predicting geopolitical events. The lack of transparency regarding the model's inner workings and the limited scope of the demo also drew criticism. Some commenters expressed interest in the potential of such a system but emphasized the need for more rigorous evaluation and open-sourcing to build trust. A few users offered alternative approaches to geopolitical forecasting, including prediction markets and leveraging existing expert analysis.
Thomas Kole's project offers a 3D reconstruction of Tenochtitlan, the capital of the Aztec empire, circa 1519. Built using Blender, the model aims for historical accuracy based on archaeological data, historical accounts, and codices. The interactive website allows users to explore the city, featuring key landmarks like the Templo Mayor, palaces, canals, and causeways, offering a vivid visualization of this pre-Columbian metropolis. While still a work in progress, the project strives to present a detailed and immersive experience of what Tenochtitlan may have looked like before the Spanish conquest.
HN users largely praised the 3D reconstruction of Tenochtitlan, calling it "beautiful," "amazing," and "impressive" work. Several commenters pointed out the value of such visualizations for understanding history and engaging with the past in a more immersive way. Some discussed the technical aspects of the project, inquiring about the software used and the challenges of creating such a detailed model. Others expressed interest in similar reconstructions of other historical cities, like Constantinople or Rome. A few commenters also delved into the historical context, discussing the Aztec empire, its conquest by the Spanish, and the modern-day location of Tenochtitlan beneath Mexico City. One commenter questioned the accuracy of certain details in the reconstruction, prompting a discussion about the available historical evidence and the inherent limitations of such projects.
Creating Augmented Reality (AR) experiences remains a complex and challenging process. The author, frustrated with the limitations of existing AR development tools, built their own visual editor called Ordinary. It aims to simplify the workflow for building location-based AR experiences by offering an intuitive interface for managing assets, defining interactions, and previewing the final product in real-time. Ordinary emphasizes collaborative editing, cloud-based project management, and a focus on location-anchored AR. The author believes this approach addresses the current pain points in AR development, making it more accessible and streamlined.
HN users generally praised the author's effort and agreed that AR development remains challenging, particularly with existing tools like Unity and RealityKit being cumbersome or limited. Several commenters highlighted the difficulty of previewing AR experiences during development, echoing the author's frustration. Some suggested exploring alternative libraries and frameworks like Godot or WebXR. The discussion also touched on the niche nature of specialized AR hardware and the potential benefits of web-based AR solutions. A few users questioned the project's long-term viability, citing the potential for Apple or another large player to release similar tools. Despite the challenges, the overall sentiment leaned towards encouragement for the author and acknowledgement of the need for better AR development tools.
Autodesk has partially restored older forum posts and IdeaStation content after significant community backlash regarding their archiving. While not all content has returned, and some functionality like search remains limited, the restored material covers a substantial portion of previously accessible information. Autodesk acknowledges the inconvenience the archiving caused and states their commitment to improving the process and platform moving forward, though a definitive timeline for full restoration and improved search functionality is yet to be determined. They encourage users to continue providing feedback.
HN commenters lament the loss of valuable technical information caused by Autodesk's forum archiving, with several noting the irony of a CAD software company failing to preserve its own data. Some praise the partial restoration, but criticize the lack of search functionality and awkward organization within the archive. Others express frustration that Autodesk hasn't learned from past mistakes and continues to undervalue its community knowledge base. The company's reliance on a single employee for the restoration is viewed with concern, highlighting the perceived fragility of the archive. Several suggest alternative archival solutions and express skepticism that Autodesk will maintain the restored content long-term. A recurring theme is the broader problem of valuable technical forums disappearing across the web.
PyVista is a Python library that provides a streamlined interface for 3D plotting and mesh analysis based on VTK. It simplifies common tasks like loading, processing, and visualizing various 3D data formats, including common file types like STL, OBJ, and VTK's own formats. PyVista aims to be user-friendly and Pythonic, allowing users to easily create interactive visualizations, perform mesh manipulations, and integrate with other scientific Python libraries like NumPy and Matplotlib. It's designed for a wide range of applications, from simple visualizations to complex scientific simulations and 3D model analysis.
HN commenters generally praised PyVista for its ease of use and clean API, making 3D visualization in Python much more accessible than alternatives like VTK. Some highlighted its usefulness in specific fields like geosciences and medical imaging. A few users compared it favorably to Mayavi, noting PyVista's more modern approach and better integration with the wider scientific Python ecosystem. Concerns raised included limited documentation for advanced features and the performance overhead of wrapping VTK. One commenter suggested adding support for GPU-accelerated rendering for larger datasets. Several commenters shared their positive experiences using PyVista in their own projects, reinforcing its practical value.
Hunyuan3D 2.0 is a significant advancement in high-resolution 3D asset generation. It introduces a novel two-stage pipeline that first generates a low-resolution mesh and then refines it to a high-resolution output using a diffusion-based process. This approach, combining a neural radiance field (NeRF) with a diffusion model, allows for efficient creation of complex and detailed 3D models with realistic textures from various input modalities like text prompts, single images, and point clouds. Hunyuan3D 2.0 outperforms existing methods in terms of visual fidelity, texture quality, and geometric consistency, setting a new standard for text-to-3D and image-to-3D generation.
Hacker News users discussed the impressive resolution and detail of Hunyuan3D-2's generated 3D models, noting the potential for advancements in gaming, VFX, and other fields. Some questioned the accessibility and licensing of the models, and expressed concern over potential misuse for creating deepfakes. Others pointed out the limited variety in the showcased examples, primarily featuring human characters, and hoped to see more diverse outputs in the future. The closed-source nature of the project and lack of a readily available demo also drew criticism, limiting community experimentation and validation of the claimed capabilities. A few commenters drew parallels to other AI-powered 3D generation tools, speculating on the underlying technology and the potential for future development in the rapidly evolving space.
Summary of Comments ( 18 )
https://news.ycombinator.com/item?id=43532551
Hacker News users discussed Apple's Cubify research, expressing excitement about its potential applications in AR/VR and robotics. Some questioned the practical use cases given the computational demands, suggesting mobile deployment would be challenging. Several commenters compared it to existing 3D modeling techniques like NeRF, noting Cubify's focus on cuboid representations might offer advantages in certain scenarios, like robot manipulation. There was also interest in the dataset used for training and the possibility of open-sourcing it. Finally, some users expressed skepticism about Apple's history of releasing research code, while others countered that their recent track record had improved.
The Hacker News post discussing Apple's "Cubify Anything" project has generated several interesting comments. Many users express excitement about the potential applications and advancements in 3D object detection.
A prevalent theme is the impressive speed and efficiency of the model, particularly its ability to generate cuboids in real-time on an iPhone. Commenters note this as a significant step towards real-world AR applications, envisioning scenarios like robots navigating cluttered environments or assisting visually impaired individuals.
Several commenters delve into the technical aspects. Some discuss the choice of using cuboids for representation, acknowledging its simplicity while questioning its limitations in capturing complex shapes accurately. Others highlight the innovative use of sparse 3D convolutions and the efficiency gains achieved through this approach.
The discussion also touches upon the broader implications for the field. Some see this as a validation of the increasing power of mobile devices for complex machine learning tasks. Others anticipate a surge in similar research and development, predicting advancements in areas like robotics, augmented reality, and 3D scene understanding.
A few commenters express curiosity about the dataset used for training and the model's robustness against different lighting conditions and object types. They also wonder about Apple's plans for releasing the code or making the technology publicly available.
Some express skepticism, questioning the practical utility of cuboid representations for complex real-world scenarios. They suggest that while impressive, the technology might be limited in its current form.
Overall, the comments reflect a mix of enthusiasm, curiosity, and cautious optimism about the implications of Apple's "Cubify Anything" project. The discussion highlights the potential for significant advancements in 3D object detection and its applications in various domains.