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In our increasingly diverse society, everyday physical interfaces often present barriers, impacting individuals across various contexts. This oversight, from small cabinet knobs to identical wall switches that can pose different contextual challenges, highlights an imperative need for solutions. Leveraging low-cost 3D-printed augmentations such as knob magnifiers and tactile labels seems promising, yet the process of discovering unrecognized barriers remains challenging because disability is context-dependent. We introduce AccessLens, an end-to-end system designed to identify inaccessible interfaces in daily objects, and recommend 3D-printable augmentations for accessibility enhancement. Our approach involves training a detector using the novel AccessDB dataset designed to automatically recognize 21 distinct Inaccessibility Classes (e.g., bar-small and round-rotate) within 6 common object categories (e.g., handle and knob). AccessMeta serves as a robust way to build a comprehensive dictionary linking these accessibility classes to open-source 3D augmentation designs. Experiments demonstrate our detector's performance in detecting inaccessible objects., Comment: CHI2024

The widespread consumer-grade 3D printers and learning resources online enable novices to self-train in remote settings. While troubleshooting plays an essential part of 3D printing, the process remains challenging for many remote novices even with the help of well-developed online sources, such as online troubleshooting archives and online community help. We conducted a formative study with 76 active 3D printing users to learn how remote novices leverage online resources in troubleshooting and their challenges. We found that remote novices cannot fully utilize online resources. For example, the online archives statically provide general information, making it hard to search and relate their unique cases with existing descriptions. Online communities can potentially ease their struggles by providing more targeted suggestions, but a helper who can provide custom help is rather scarce, making it hard to obtain timely assistance. We propose 3DPFIX, an interactive 3D troubleshooting system powered by the pipeline to facilitate Human-AI Collaboration, designed to improve novices' 3D printing experiences and thus help them easily accumulate their domain knowledge. We built 3DPFIX that supports automated diagnosis and solution-seeking. 3DPFIX was built upon shared dialogues about failure cases from Q&A discourses accumulated in online communities. We leverage social annotations (i.e., comments) to build an annotated failure image dataset for AI classifiers and extract a solution pool. Our summative study revealed that using 3DPFIX helped participants spend significantly less effort in diagnosing failures and finding a more accurate solution than relying on their common practice. We also found that 3DPFIX users learn about 3D printing domain-specific knowledge. We discuss the implications of leveraging community-driven data in developing future Human-AI Collaboration designs., Comment: CSCW2024

Instance detection (InsDet) is a long-lasting problem in robotics and computer vision, aiming to detect object instances (predefined by some visual examples) in a cluttered scene. Despite its practical significance, its advancement is overshadowed by Object Detection, which aims to detect objects belonging to some predefined classes. One major reason is that current InsDet datasets are too small in scale by today's standards. For example, the popular InsDet dataset GMU (published in 2016) has only 23 instances, far less than COCO (80 classes), a well-known object detection dataset published in 2014. We are motivated to introduce a new InsDet dataset and protocol. First, we define a realistic setup for InsDet: training data consists of multi-view instance captures, along with diverse scene images allowing synthesizing training images by pasting instance images on them with free box annotations. Second, we release a real-world database, which contains multi-view capture of 100 object instances, and high-resolution (6k x 8k) testing images. Third, we extensively study baseline methods for InsDet on our dataset, analyze their performance and suggest future work. Somewhat surprisingly, using the off-the-shelf class-agnostic segmentation model (Segment Anything Model, SAM) and the self-supervised feature representation DINOv2 performs the best, achieving >10 AP better than end-to-end trained InsDet models that repurpose object detectors (e.g., FasterRCNN and RetinaNet)., Comment: Accepted by NeurIPS 2023, Datasets and Benchmarks Track

With the continuous growth of online 3D printing community and the democratization of 3D printers, growing number of users start sharing their own 3D designs on open platforms, enabling a wide audience to search, download, and 3D print models for free. Although sharing is mostly for altruistic reasons at first, open platforms had also created potential job opportunities to compensate creative labors. This paper analyzes new job opportunities emerged in online 3D printing social platforms and patterns of seeking compensations, and reveals various motivations for posting creative content online. We find that offering exclusive membership through subscriptions, selling final products or printing services through web stores, and using affiliate links are primary means of earning profits, while there exist gaps between creators' expectations and realities. We show that various socio-economic promises emerged, leading to a win-win situation for both creators to gain extra income and audiences to have access to more quality content. We also discuss future challenges that need to be addressed, such as ethical use of opensource content.

While people with visual impairments are interested in artwork as much as their sighted peers, their experience is limited to few selective artworks that are exhibited at certain museums. To enable people with visual impairments to access and appreciate as many artworks as possible at ease, we propose an online art gallery that allows users to explore different parts of a painting displayed on their touchscreen-based devices while listening to corresponding verbal descriptions of the touched part on the screen. To investigate the scalability of our approach, we first explored if anonymous crowd who may not have expertise in art are capable of providing visual descriptions of artwork as a preliminary study. Then we conducted a user study with 9 participants with visual impairments to explore the potential of our system for independent artwork appreciation by assessing if and how well the system supports 4 steps of Feldman Model of Criticism. The findings suggest that visual descriptions of artworks produced by an anonymous crowd are sufficient for people with visual impairments to interpret and appreciate paintings with their own judgments which is different from existing approaches that focused on delivering descriptions and opinions written by art experts. Based on the lessons learned from the study, we plan to collect visual descriptions of a greater number of artwork and distribute our online art gallery publicly to make more paintings accessible for people with visual impairments., Comment: 17 pages, 8 figures, UAIS, Universal Access and Information Society, Springer

Algorithmic content moderation manages an explosive number of user-created content shared online everyday. Despite a massive number of 3D designs that are free to be downloaded, shared, and 3D printed by the users, detecting sensitivity with transparency and fairness has been controversial. Although sensitive 3D content might have a greater impact than other media due to its possible reproducibility and replicability without restriction, prevailed unawareness resulted in proliferation of sensitive 3D models online and a lack of discussion on transparent and fair 3D content moderation. As the 3D content exists as a document on the web mainly consisting of text and images, we first study the existing algorithmic efforts based on text and images and the prior endeavors to encompass transparency and fairness in moderation, which can also be useful in a 3D printing domain. At the same time, we identify 3D specific features that should be addressed to advance a 3D specialized algorithmic moderation. As a potential solution, we suggest a human-in-the-loop pipeline using augmented learning, powered by various stakeholders with different backgrounds and perspectives in understanding the content. Our pipeline aims to minimize personal biases by enabling diverse stakeholders to be vocal in reflecting various factors to interpret the content. We add our initial proposal for redesigning metadata of open 3D repositories, to invoke users' responsible actions of being granted consent from the subject upon sharing contents for free in the public spaces., Comment: 9 pages, 2 figures, TExSS, ACM IUI 2021 Workshops