Recommended
More Related Content
Similar to CASA2016 (Takuya Yamakawa) Efficient Simulation of Furniture Layout Taking into Account Lighting Environment
Similar to CASA2016 (Takuya Yamakawa) Efficient Simulation of Furniture Layout Taking into Account Lighting Environment (20)
Recently uploaded
Recently uploaded (20)
CASA2016 (Takuya Yamakawa) Efficient Simulation of Furniture Layout Taking into Account Lighting Environment
- 1. Efficient Simulation of Furniture Layout Taking into Account Lighting Environment Takuya Yamakawa, Yoshinori Dobashi, Tsuyoshi Yamamoto 1
- 2. Table of Contents Introduction Related Works Proposed Method Results Conclusion and future plans 2
- 4. Introduction Technologies for designing every day objects Create everyday objects that satisfy his or her particular needs Clothing design, interior and furniture design, architecture design, etc. Apply to furniture layout. 4
- 5. Introduction Furniture layout design is challenging. Several methods have been proposed. [Merrell et al 11] [Yu et al 11] [Xu et al 02] Functional criterion Visual criterion The lighting environment is also an important factor. Lighting rules for a room (e.g. recommend illuminance) Suggest a furniture layout design method taking into account lighting environment. 5
- 7. Related Works [Xu et al 02] [Germer et al 09] [Liu et al 15] Furniture Layout Design For large space room [Xu et al 02] Taking into account real-time walkthroughs [Germer et al 09] Using crowd-sourced datasets for styles of furniture layout [Liu et al 15] 7 No method taking into account lighting environment.
- 8. Related Works Lighting Design [Schwarz et al 14][Shacked et al 01] [Okabe et al 07] Computing various lighting parameters [Shacked et al 01] No works for furniture layout under fixed lighting parameters. Desired intensities painted by user [Okabe et al 07] The lighting design of procedurally modeled building [Schwarz et al 14] 8
- 10. Overview Add a new cost function to evaluate the lighting Fast Evaluation of illuminance function Evaluate inter-reflection suitable for furniture arrangement simulation Lighting Design Furniture Layout Simulation [Merrell et al 11] Based on Merrell et al method. Minimizing a cost function Use a Markov chain Monte Carlo sampler to suggest optimized layouts 10
- 11. Furniture layout simulation Merrell et al method conversation mpd(F), mcd(F), mca(F) alignment mwa(F),mfa(F) 𝑐 𝐹 = 𝑖 𝑤𝑖 𝑚𝑖(𝐹) cost function 𝑝 𝐹 = 1 𝑍 exp(−𝛽𝑐 𝐹 ) density function Method - Employ a Markov chain Monte Carlo sampler to explore the function. - Use the Metropolis-Hasting algorithm. 11 Functional and visual criteria
- 12. Lighting Environment for Furniture Layout How to evaluate lighting for interior designOur method Room function Recommend illuminance[lx] Studying, Reading 1000 ~ 500 Eating, Cooking 500 ~ 220 Playing, Gatherings 220 ~ 150 General lighting 150 ~ 75 Sleeping, Private room 30 ~ 10 12
- 13. Lighting Environment for Furniture Layout A new cost function for lighting environment Room function Recommend illuminance[lx] Playing, Gatherings 220 ~ 150 light source Our method L(f) : Average illuminance [lx] reflection from wall direct light higher order reflections 13 1 (Lm(f),LM(f)) : the range for the recommended illuminance
- 14. reflection from wall 𝐿 𝑑,𝑤 𝑓 direct light 𝐿 𝑎𝑚𝑏(𝑓) higher order reflections light source Lighting Environment for Furniture Layout How to calculate illuminance L(f)Our method 14 Problem Need fast lighting calculation for furniture layout system. Many iterations are required. Calculating all reflections takes too much time. Our solution Recomposing illuminance into two terms (Ld,w(f) and Lamb(f)) Ld,w(f) has large contribution to L(f). Approximating inter-reflection as Lamb(f) because Lamb(f) has less contribution than Ld,w(f). Ignore the effects of shadows.
- 15. Lighting Environment for Furniture Layout How to calculate illuminance L(f) light source Our method 𝐿 𝑓 = 𝐿 𝑑,𝑤 𝑓 + 𝐿 𝑎𝑚𝑏(𝑓) Illuminance function reflection from wall Ld,w(f)： average illuminance due to the direct light and reflections from walls 𝐿 𝑑,𝑤 𝑓 direct light 𝐿 𝑎𝑚𝑏(𝑓) higher order reflections Lamb(f)： average illuminance due to higher order reflection 15
- 16. Evaluation of Ld,w light source reflection from wall Our method direct light 𝐿 𝑑,𝑤 𝑓 Lighting Environment for Furniture Layout The view from the top qf Pf P s S T t u v (=A) B C D room Φ：Luminous flux Average illuminance 𝐿 𝑑,𝑤 = Φ 𝐴 A: Area of the top surface 16
- 17. Evaluation of Ld,w Our method Lighting Environment for Furniture Layout qf Pf P s S T t u v (=A) B C D room 𝐿 𝑑,𝑤 𝑃𝑓, 𝜃𝑓 = 1 𝑆𝑇 0 𝑆 0 𝑇 𝜙 𝑠, 𝑡, 𝜃𝑓 𝑑𝑠𝑑𝑡 Average illuminance from the direct light and reflection from wall 𝐿 𝑑,𝑤 = Φ 𝐴 Φ ： Luminous flux A : Area of the top surface 𝜙 𝑠, 𝑡, 𝜃𝑓 = 𝜙 𝑑 𝑠, 𝑡, 𝜃𝑓 + 𝜙 𝑤(𝑠, 𝑡, 𝜃𝑓) 𝜙 𝑑, 𝜙 𝑤 ： the illuminance at point P 17 Precompute Ld,w for all positions Pf and orientations θf
- 18. Summed area table algorithmOur method Lighting Environment for Furniture Layout qf Pf P s S T t u v (=A) B C D room Φ 𝑢, 𝑣, 𝜃𝑓 = 0 𝑢 0 𝑣 𝜙 𝑢∗, 𝑣∗, 𝜃𝑓 𝑑𝑢∗ 𝑑𝑣∗ 18 zero
- 19. Summed area table algorithmOur method Lighting Environment for Furniture Layout qf Pf P s S T t u v (=A) B C D room Φ 𝐶 19
- 20. Summed area table algorithmOur method Lighting Environment for Furniture Layout qf Pf P s S T t u v (=A) B C D room Φ 𝐴 20
- 21. Summed area table algorithmOur method Lighting Environment for Furniture Layout qf Pf P s S T t u v (=A) B C D room Φ 𝐷 21
- 22. Summed area table algorithmOur method Lighting Environment for Furniture Layout qf Pf P s S T t u v (=A) B C D room Φ 𝐵 22
- 23. Summed area table algorithmOur method Lighting Environment for Furniture Layout qf Pf P s S T t u v (=A) B C D room 𝐿 𝑑,𝑤 𝑃𝑓, 𝜃𝑓 = 1 𝑆𝑇 ΦC + Φ 𝐴 − ΦB − ΦD 23
- 24. Evaluation of Lamb light source higher order reflections Our method 𝐿 𝑎𝑚𝑏(𝑓) Assume Lamb to be a const value in a room. Radiosity algorithm to compute the inter-reflections Calculating inter-reflections takes too much time. Preparing a set of example layouts. Lighting Environment for Furniture Layout 24
- 25. Using a set of example layoutsOur method Lighting Environment for Furniture Layout 25 room Example 1 room Example 2 room Example 3 L1(f): radiosity solution
- 26. Using a set of example layoutsOur method Lighting Environment for Furniture Layout 25 room Example 1 room Example 2 room Example 3 L2(f): radiosity solution
- 27. Using a set of example layoutsOur method Lighting Environment for Furniture Layout 25 room Example 1 room Example 2 room Example 3 L3(f): radiosity solution
- 28. Using a set of example layoutsOur method Lighting Environment for Furniture Layout 25 room Example 1 room Example 2 room Example 3 N k kamb fL N fL 1 )( 1 )(
- 29. Lighting Environment for Furniture Layout Our method Evaluation of Lamb Full radiosity solution 26 Higher order inter-reflection 【Coffee Table】 average : 34.7 [lx] standard deviation : 3.0 [lx] 【Dining Table】 average : 32.8 [lx] standard deviation : 1.2 [lx]
- 30. Results 27
- 31. Computational Evaluation CPU : Intel Core i7 Graphics API : OpenGL Memory : 8GB Number of iterations:10,000 times Time: 1~2 sec Time of precomputation (summed area table): about 30 sec Rendering : Autodesk Maya 2016 28
- 32. Furniture objects Recommended illuminance (Lm,LM) Dining Table Coffee Table Eating, Cooking (500 ~ 220) Playing, Gatherings (220 ~ 150) Sleeping, Private room (30 ~ 10) 29
- 33. Eight light sources on the ceiling 30 downlight downlight downlight downlight
- 37. With and without our method With our method Without our method Placed under the lighting equipment Not Placed under the lighting equipment Dining Table Coffee Table 263[lx] (220~500) 197[lx] (150~220) Dining Table Coffee Table 97[lx] (220~500) 174[lx] (150~220) 34
- 38. Other Example 35 Bright spot in the center Bright spot in the right Indirect lighting
- 39. Average illuminance of the top surface Dining Table Coffee Table 298[lx] (220~500) 153[lx](150~220) Dining Table Coffee Table 346[lx] (220~500) 159[lx](150~220) Dining Table Coffee Table 244[lx] (220~500) 22[lx](10~30) The illuminance on the dining and coffee tables are in the recommended range 36
- 40. Conclusion and future plans 37
- 41. Conclusion Computing furniture layout that takes lighting conditions into account A new cost term using the average illuminance based on the lighting design guidelines A fast method using summed area table algorithm Succeed in designing furniture layout that is optimal in terms of functionality, visual composition, and lighting environment. 38
- 42. Future plans Consideration of natural light sources Sunlight, skylight etc. Computing the inter-reflections from windows Apply to real world interior design and compare our method with the manual layouts 39
- 43. END 40
Editor's Notes
- ゆっくりしゃべろう！！！ Thank you Mr. Chairman. Good afternoon. Today, I will be talking about “Efficient Simulation of Furniture Layout Taking into Account Lighting Environment”.
- This slide shows the contents of my presentation.
- First, I’d like to begin with background and motivation of this research as an introduction.
- Recently in computer graphics, many researchers have developed computer-aided systems for designing everyday objects. This enables people to create everyday objects that satisfy his or her particular needs. So we can apply this technology to many everyday things, such as clothing design, interior and furniture design, architecture design and so on. () In this presentation, we apply this technology to furniture layout design.
- Furniture placement is challenging because it requires jointly optimizing a variety of functional and visual criteria. So, many researchers proposed several methods that help people to arrange the furniture layout. () The lighting environment, such as positions and colors of light sources, is also an important factor. For example, there are some lighting rules, such as positions, colors and recommend illuminance. () So, we suggest a furniture layout taking into account lighting environment.
- Next, I’d like to talk about related work.
- Many researchers have proposed various methods of furniture layout design. ()This method computes the furniture layout for large spaces. ()This method suggests a layout taking into account real-time walkthroughs. ()This method used crowd-sourced datasets for styles of furniture layout. ()However, there is no method taking into account lighting environment.
- In this slide, we show some methods of lighting design. ()This paper developed an automatic method for computing various lighting parameters. ()This method proposed a method for inversely computing environmental lighting from the desired intensities painted by the user. ()This paper presented a system for the lighting design of procedurally modeled buildings. ()However, there is no method for furniture layout under fixed lighting parameters.
- Next, I’d like to talk about our proposed method.
- There are two factors in our method. ()Furniture layout simulation and Lighting design. ()Our furniture layout simulation is based on Merrell et al method. Merrell’s method determines the furniture layout by minimizing a cost function. And this method uses a Markov chain Monte Carlo sampler to suggest optimized layouts. ()In our lighting design, we add a new cost function to evaluate the lighting in a room. And we propose a fast evaluation method for illuminance function. And we propose an efficient method for evaluating inter-reflection that is suitable for layout simulation.
- In this slide, we show the overview of Merrell’s furniture layout system. There are two criteria, functional and visual criteria. ()For example, this figure shows a factor in functional criterion, a conversation factor. It evaluates suitable distance of two pieces of furniture in order to talk comfortably. ()This figure shows a factor in visual criterion, an alignment factor. It evaluates the orientation of the furniture items relative to each other and walls of the room. ()And then a cost function is defined by combining these terms. ()()So they employ a Markov chain Monte Carlo sampler to explore the function. Specifically, they define a Boltzmann-like density function and use the Metropolis-Hastings algorithm to explore density function.
- In this slide, we will explain our idea of lighting environment for furniture layout system. There is a close relationship between the function of a room and the lighting environment. ()This table shows a list of recommended illuminance for different room function found in a typical lighting design guideline. The recommended illuminance changes depending on the human activities. So we employ this recommended illuminance for furniture layout simulation.
- We will explain a new cost function using recommended illuminance. First, as shown in this figure, a furniture item is illuminated from various direction, ()such as from direct light, reflection from wall and higher order reflections. ()And we define a new cost function to evaluate illuminance. ()This L(f) means average illuminance of the top surface of the furniture item. ()And this graph shows the function t and the maximum value is one when L(f) is within the recommended range (Lmin to Lmax). ()For example, if a furniture item is used for playing and gathering, this recommended range is (150 to 220). ()So when it’s illuminance is within these range, function t has the maximum value and the position of this furniture item is appropriate from this illuminance point of view.
- In furniture layout simulation, there are some problems about calculating illuminance. ()We need fast lighting calculation for furniture layout system because many iterations are required. And generally calculating all reflections takes too much time. ()So, in our method, we recompose illuminance into two terms, direct light and reflections from walls term Ldw and ambient term Lambient. Ldw has large contribution to illuminance L(f). And we approximate inter-reflection as Lambient because Lambient has less contribution than Ldw. In our simulation, we ignore the effects of shadows because calculating shadows takes too much time.
- Now we will explain how to calculate illuminance L(f) in our method. ()Our illuminance function is divided into two terms. ()One is the average illuminance due to the direct light and reflections from walls ()The other is the average illuminance due to higher order reflection.
- First, we will explain the evaluation of Ldw. As shown in this figure, a furniture item is illuminated from direct light and reflections from walls. ()Then, let’s look at the room from the top. ()And we define the average illuminance as this equation. A is the area of the top surface of a furniture item. ()And we define Phi as Luminous flux.
- So, we will explain how to calculate Ldw in detail. ()Ldw is expressed by this equation in our method. ()Area of the top surface A equals ST as shown in the left figure. ()And luminous flux is defined as the integration over the top surface. ()And the illuminance small phi is defined as the sum of the illuminance from direct light and the reflections from walls. ()So in our method, we precompute Ldw for all positions P and orientations theta f of each furniture iterm.
- In our method, in order to precompute the integration, we used summed area table algorithm. For example, a furniture item is placed like this figure. And we calculate bounding box of this floor which is rotated by the same orientation of a furniture item, theta f. And this bounding box is shown by this dashed line. ()And we divide this bounding box at a regular interval. ()Then, we precompute this integration for every points. ()If points are out of the room, we define this integration as zero.
- Then, we calculate the integration of the corner point of a furniture item, Phi C.
- And calculate Phi A.
- And Phi D.
- And Phi B.
- By using this method, We can calculate the integration by ()Phi C plus ()Phi A minus ()Phi B minus ()Phi D. So, we can efficiently get the average illuminance Ldw.
- Next, we will explain how to evaluate higher order reflection Lambient. ()In our method, we assume Lamb to be a const value in a rectangular room. ()So, we precompute Lamb by using radiosity algorithm. ()Calculating inter-reflections takes too much time. ()So we prepare a set of examples, and determine Lamb from them.
- For example, we use a set of example layouts. ()Example 1 and () example 2 and () example3. ()And we calculate radiosity solution L1 in example 1.
- And calculate L2 in example 2.
- And calculate L3 in example 3.
- And finally find the mean of every radiosity result.
- In this slide, we show the results of the calculation of Lamb by using 30 patterns of furniture layout. The shape of each furniture item is replaced by its bounding box. The upper figure shows the full rasiosity solution. And the lower figure shows the higher order inter-reflection. ()This graph shows the calculation of Lamb by using 30 types of furniture layout. The upper graph shows the case of coffee table. And the lower graph shows the case of dining table. ()In the case of coffee table, the average of Lamb is 34.7 lx and the standard deviation is 3.0 lx. ()In the case of dining table, the average of Lamb is 32.8 lx and the standard deviation is 1.2 lx.
- Next, I’d like to talk about our results.
- This slide shows our environment used for the following examples. We experiment the following examples for 10,000 iterations, which takes about a few seconds. Time of precomputation is about 30 seconds.
- In our simulation, we used this set of furniture items. And recommended illuminance is shown in this table.
- In this slide, we explain the lighting environment. Left figure shows positions of light sources on the ceiling. Right figure shows the images of rooms without furniture items. In the upper example, left is brighter than right. In the lower example, right is brighter than left.
- And we additionally tested these two lighting conditions. In the upper example, we placed a single downlight at the center. In the lower example, we placed the lighting sources to produce night atmosphere.
- This is the results. In this example, left side is brighter than right side. (動画クリック)Our system suggests three furniture layouts and user can select the favorite layout. Dining table is placed at the left brighter spot and coffee table is placed at the right darker spot. In this example, right side is brighter than left side. (動画クリック) We think these furniture layouts are neat and balanced and you can see dinning and coffee table are placed at the appropriate spot.
- We show other examples. (動画クリック) You can see that all furniture items try to be placed at the center brighter spot. Our system finds a layout that satisfies both visual quality and lighting condition as shown in this video. (動画クリック) This shows an example where indirect lighting is dominated. Even with this indirect lighting environment, dining table is placed in the brighter spot and coffee table is placed in the darker spot.
- ()In our method, you can see that furniture items are placed under the lighting equipment. ()However, without our method, you can see that furniture items are not placed under the lighting equipment. ()Additionally, when we calculate the illuminance on the top surface of the dining and coffee table, with our method the illuminances are within the recommended illuminance range. However, without our method the illuminance of dining table is out range of the recommended illuminance.
- These are other example layouts. Bright spot in the center. Bright spot in the right. And indirect lighting. You can see that all of them are successfully placed.
- We calculated the illuminance of dining and coffee table in all examples. ()The illuminance on the dining and coffee tables are in the recommended range.
- Finally, I’d like to conclude this presentation.
- We proposed a method for computing furniture layout that takes lighting conditions into account. We add a new cost term using the average illuminance based on the lighting design guidelines. And we proposed a fast method of calculating the average illuminance by using summed area table algorithm. And we succeeded in designing furniture layout that is optimal in terms of functionality, visual composition, and lighting environment.
- In our future work, we’d like to take into consideration natural light sources, such as sunlight and skylight. And we’d like to compute the inter-reflections from windows. In addition, we’d like to apply our method to real world interior design and compare our method with the manual layouts.
- Thank you very much for your attention.