Designers often create physical works-like prototypes early in the product development cycle to explore possible mechanical architectures for a design. Yet, creating functional prototypes requires time and expertise, which discourages rapid design iterations. Designers must carefully specify part and joint parameters to ensure that parts move and fit and together in the intended manner. We present an interactive system that streamlines the process by allowing users to annotate rough 3D models with high-level functional relationships (e.g., part A fits inside part B). Based on these relationships, our system optimizes the model geometry to produce a working design. We demonstrate the versatility of our system by using it to design a variety of works-like prototypes.

a) Our Method, 138 rpp, 3.14 sec (b) Equal time, 176 rpp, 3.11 sec (c) Our method, 138 rpp, 3.14 sec (d) Eq. quality, 5390 rpp, 130 sec (e) No factoring, 138 rpp, 3.10 sec defocus filter (pixels) num. primary rays (f) Primary filter and rpp 1 20 1 64 indirect filter (pixels) num. indirect rays (g) Indirect filter and rpp Figure 1: (a) The CHESS scene, with defocus blur, area light direct and indirect illumination, rendered at 900×1024 with an average 138 atomic rays per pixel (rpp), in 3.14 sec on an NVIDIA Titan GPU. We compare to different methods in the insets. Readers are encouraged to zoom into the PDF to examine the noise. The top row inset is a sharp in-focus region while the other two regions are defocus blurred; all insets include noisy direct and indirect illumination. In (b) we compare to equal time stratified Monte Carlo (MC) sampling with 176 rpp; in (c), Our method; and in (d), Equal quality MC with 5390 rpp is 40× slower. One of the key contributions of our method is factoring of texture and irradiance, so that irradiance can be pre-filtered before combining with texture. Without factoring, the defocus filter cannot remove noise for in-focus regions as shown in (e), top inset. In (f) and (g) top row, we show filter size for texture and indirect irradiance. Black pixels indicate that factoring cannot be used and irradiance cannot be pre-filtered. In the bottom row we show number of primary rays and indirect samples respectively. Our method uses separate sampling rates and filters for primary and secondary effects which makes it more effective. AbstractMonte Carlo (MC) ray-tracing for photo-realistic rendering often requires hours to render a single image due to the large sampling rates needed for convergence. Previous methods have attempted to filter sparsely sampled MC renders but these methods have high reconstruction overheads. Recent work has shown fast performance for individual effects, like soft shadows and indirect illumination, using axis-aligned filtering. While some components of light transport such as indirect or area illumination are smooth, they are often multiplied by high-frequency components such as texture, which prevents their sparse sampling and reconstruction.We propose an approach to adaptively sample and filter for simultaneously rendering primary (defocus blur) and secondary (soft shadows and indirect illumination) distribution effects, based on a multi-dimensional frequency analysis of the direct and indirect illumination light fields. We describe a novel approach of factoring texture and irradiance in the presence of defocus blur, which allows for pre-filtering noisy irradiance when the texture is not noisy. Our approach naturally allows for different sampling rates for primary and secondary effects, further reducing the overall ray count. While the theory considers only Lambertian surfaces, we obtain promising results for moderately glossy surfaces. We demonstrate 30× sampling rate reduction compared to equal quality noise-free MC. Combined with a GPU implementation and l...

High-quality hand-made furniture often employs intrinsic joints that geometrically interlock along mating surfaces. Such joints increase the structural integrity of the furniture and add to its visual appeal. We present an interactive tool for designing such intrinsic joints. Users draw the visual appearance of the joints on the surface of an input furniture model as groups of two-dimensional (2D) regions that must belong to the same part. Our tool automatically partitions the furniture model into a set of solid 3D parts that conform to the user-specified 2D regions and assemble into the furniture. If the input does not merit assemblable solid 3D parts, then our tool reports the failure and suggests options for redesigning the 2D surface regions so that they are assemblable. Similarly, if any parts in the resulting assembly are unstable, then our tool suggests where additional 2D regions should be drawn to better interlock the parts and improve stability. To perform this stability analysis, we introduce a novel variational static analysis method that addresses shortcomings of the equilibrium method for our task. Specifically, our method correctly detects sliding instabilities and reports the locations and directions of sliding and hinging failures. We show that our tool can be used to generate over 100 joints inspired by traditional woodworking and Japanese joinery. We also design and fabricate nine complete furniture assemblies that are stable and connected using only the intrinsic joints produced by our tool.

High-quality hand-made furniture often employs intrinsic joints that geometrically interlock along mating surfaces. Such joints increase the structural integrity of the furniture and add to its visual appeal. We present an interactive tool for designing such intrinsic joints. Users draw the visual appearance of the joints on the surface of an input furniture model as groups of two-dimensional (2D) regions that must belong to the same part. Our tool automatically partitions the furniture model into a set of solid 3D parts that conform to the user-specified 2D regions and assemble into the furniture. If the input does not merit assemblable solid 3D parts, then our tool reports the failure and suggests options for redesigning the 2D surface regions so that they are assemblable. Similarly, if any parts in the resulting assembly are unstable, then our tool suggests where additional 2D regions should be drawn to better interlock the parts and improve stability. To perform this stability analysis, we introduce a novel variational static analysis method that addresses shortcomings of the equilibrium method for our task. Specifically, our method correctly detects sliding instabilities and reports the locations and directions of sliding and hinging failures. We show that our tool can be used to generate over 100 joints inspired by traditional woodworking and Japanese joinery. We also design and fabricate nine complete furniture assemblies that are stable and connected using only the intrinsic joints produced by our tool.