Traditional carpentry faces a critical shortage of skilled workers due to limited opportunities for potential apprentices to access onsite woodworking experience. Through expert interview, we learned the importance of hammering sound to judge the precision in Kigumi assembly, as master carpenters rely on differences between soft sound and sharp sound without relying on visual. This paper presents Sound of Kigumi (SoK), a playful VR system for inexperienced users to casually experience sound sensory skills through loop of hammering and chiseling. In SoK, users listen to hammering sound in relation to tightness, assess the precision of their work, and return to chiseling for further adjustments. Furthermore, SoK implements pseudo-haptic feedback by visually modifying hammering resistance based on chiseling progress. Expert evaluation indicated SoK replicates the hammering process and serves as effective introductory tool, and user feedback confirmed SoK provides an immersive woodworking experience and effective Kigumi learning.

Remote sensing for observing and recording our surroundings is becoming mainstream. Technologies, such as light, detection, and ranging (LiDAR), are now part of consumer mobile devices and provide a variety of novel interaction opportunities with the environment. Mobile remote sensing also provides affordances for crowdsourcing through location-based applications such as games and gamified systems. While such use cases today are technologically feasible, there is a lack of understanding of how and what kinds of interactions and applications would be both (1) engaging and motivating for users and also (2) maximize the volume and quality of the data being gathered. In this study, we investigate these challenges by developing and testing four gamified augmented reality prototypes that use LiDAR for collecting point cloud data during location-based gaming. Through field testing, interviews, and surveys with 21 participants, followed by reflexive thematic analysis, we identified five themes of dynamics, which exemplify tensions and challenges to designing gamified AR crowdsourcing. The findings primarily point to hazards in design that may undermine user motivation as well as constraints of the environments themselves in facilitating and affording meaningful and rich (gameful) interaction.

Text input in MR(Mixed Reality) provides options for users to model in details instead of just placing objects, however, 3D modeling with text input costs computation and takes time. To overcome this hurdle, we let the text-image model judge 3D layout of furniture parts. Since vanilla text-image model can not judge furniture stability, we tested two approaches: 1. combine with geometric loss, and 2. fine-tuning the model. We report the comparison of these two approaches and discuss further development for MR integration of our system.

Abstract We propose a method for inferring the internal anisotropic volumetric texture of a given wood block from annotated photographs of its external surfaces. The global structure of the annual ring pattern is represented using a continuous spatial scalar field referred to as the growth time field (GTF). First, we train a generic neural model that can represent various GTFs using procedurally generated training data. Next, we fit the generic model to the GTF of a given wood block based on surface annotations. Finally, we convert the GTF to an annual ring field (ARF) revealing the layered pattern and apply neural style transfer to render orientation‐dependent small‐scale features and colors on a cut surface. We show rendered results of various physically cut real wood samples. Our method has physical and virtual applications such as cut‐preview before subtractive fabricating solid wood artifacts and simulating object breaking.

We present a procedural framework for modeling the annual ring pattern of solid wood with knots. Although wood texturing is a well-studied topic, there have been few previous attempts at modeling knots inside the wood texture. Our method takes the skeletal structure of a tree log as input and produces a three-dimensional scalar field representing the time of added growth, which defines the volumetric annual ring pattern. First, separate fields are computed around each strand of the skeleton, i.e., the stem and each knot. The strands are then merged into a single field using smooth minimums. We further suggest techniques for controlling the smooth minimum to adjust the balance of smoothness and reproduce the distortion effects observed around dead knots. Our method is implemented as a shader program running on a GPU with computation times of approximately 0.5 s per image and an input data size of 600 KB. We present rendered images of solid wood from pine and spruce as well as plywood and cross-laminated timber (CLT). Our results were evaluated by wood experts, who confirmed the plausibility of the rendered annual ring patterns. Link to code: https://github.com/marialarsson/procedural_knots.

One of the challenges of full autonomy is to have robots capable of manipulating its current environment to achieve another environment configuration. This paper is a step towards this challenge, focusing on the visual understanding of the task. Our approach trains a deep neural network to represent images as measurable features that are useful to estimate the progress (or phase) of a task. The training uses numerous variations of images of identical tasks when taken under the same phase index. The goal is to make the network sensitive to differences in task progress but insensitive to the appearance of the images. To this end, our method builds upon Time-Contrastive Networks (TCNs) to train a network using only discrete snapshots taken at different stages of a task. A robot can then solve long-horizon tasks by using the trained network to identify the progress of the current task and by iteratively calling a motion planner until the task is solved. We quantify the granularity achieved by the network in two simulated environments. In the first, to detect the number of objects in a scene and in the second to measure the volume of particles in a cup. Our experiments leverage this granularity to make a mobile robot move a desired number of objects into a storage area and to control the amount of pouring in a cup.

We present Tsugite - an interactive system for designing and fabricating wood joints for frame structures. To design and manually craft such joints is difficult and time consuming. Our system facilitates the creation of custom joints by a modeling interface combined with computer numerical control (CNC) fabrication. The design space is a 3D grid of voxels that enables efficient geometrical analysis and combinatorial search. The interface has two modes: manual editing and gallery. In the manual editing mode, the user edits a joint while receiving real-time graphical feedback and suggestions provided based on performance metrics including slidability, fabricability, and durability with regard to the direction of fiber. In the gallery mode, the user views and selects feasible joints that have been pre-calculated. When a joint design is finalized, it can be manufactured with a 3-axis CNC milling machine using a specialized path planning algorithm that ensures joint assemblability by corner rounding. This system was evaluated via a user study and by designing and fabricating joint samples and functional furniture.

While tree trunks are standardized as lumber, branches are typically chipped or burned. This paper proposes a workflow to upcycle such mundane and diverse natural material to architectural elements. Introducing an online design interface, we let users participate in the design and fabrication workflow from collecting branches to CNC milling. The branches are first scanned, and then key geometrical features are extracted and uploaded to the online game "BranchConnect". This application lets multiple non-expert users create 2D-layouts. At the point of intersection between two branches, the geometry of a lap joint and its cutting path are calculated on-the-fly. A CNC router mills out the joints accordingly, and the branches are assembled manually. Through this workflow, users go back-and-forth between physical and digital representations of tree branches. The process was validated with two case studies.

Predominately, robotic construction is applied as prefabrication in structured indoor environments with standard building materials. Our work, on the other hand, focuses on utilizing irregular materials found on-site, such as rubble and rocks, for autonomous construction. We present a pipeline to detect arbitrarily placed objects in a scene and form a structure out of the detected objects. The next best stacking pose is selected using a searching method employing gradient descent with random initial orientations, exploiting a physics engine. This approach is validated in an experimental setup using a robotic manipulator by constructing balancing vertical stacks without mortars and adhesives. We show the results of eleven consecutive trials to form such towers autonomously using four arbitrarily in front of the robot placed rocks.

Predominately, robotic construction is applied as prefabrication in structured indoor environments with standard building materials. Our work, on the other hand, focuses on utilizing irregular materials found on-site, such as rubble and rocks, for autonomous construction. We present a pipeline that detects randomly placed objects in a scene that are used by our next best stacking pose searching method employing gradient descent with a random initial orientation, exploiting a physics engine. This approach is validated in an experimental setup using a robotic manipulator by constructing balancing vertical stacks without mortars and adhesives. We show the results of eleven consecutive trials to form such towers autonomously using four arbitrarily in front of the robot placed rocks.

Recent digital fabrication tools have opened up accessibility to personalized rapid prototyping; however, such tools are limited to product-scale objects. The materials currently available for use in 3D printing are too fine for large-scale objects, and CNC gantry sizes limit the scope of printable objects. In this paper, we propose a new method for printing architecture-scale objects. Our proposal includes three developments: (i) a construction material consisting of chopsticks and glue, (ii) a handheld chopstick dispenser, and (iii) a printing guidance system that uses projection mapping. The proposed chopstickglue material is cost effective, environmentally sustainable, and can be printed more quickly than conventional materials. The developed handheld dispenser enables consistent feeding of the chopstickglue material composite. The printing guidance system --- consisting of a depth camera and a projector --- evaluates a given shape in real time and indicates where humans should deposit chopsticks by projecting a simple color code onto the form under construction. Given the mechanical specifications of the stickglue composite, an experimental pavilion was designed as a case study of the proposed method and built without scaffoldings and formworks. The case study also revealed several fundamental limitations, such as the projector does not work in daylight, which requires future investigations.