SIGGRAPH 2011 ASIA Preview Seminar

Rendering: Accuracy and Efficiency

Shinichi YamashitaTriaxis Co.,Ltd.

Rendering: Accuracy and Efficiency

Paper List Displacement Interpolation Using Lagrandian Mass Trans

port Nicolas Bonneel et al. (National Institute for Research in Computer Science and Control, Canada)

Adaptive Sampling and Reconstruction using Greedy Error MinimizationFabrice Rousselle et al. (University of Bern, Switzerland)

T&I Engine: Traversal and Intersection Engine for Hardware Accelerated Ray TracingJae-Ho Nah et al. (Yonsei University, South Korea)

Coherent Parallel HashingIsmael Garcia Fernandez et al. (University of Girona, Spain)

Displacement Interpolation Using Lagrandian Mass Transport Abstract

Linear Interpolation: can’t capture transitional motion Displacement Interpolation: works based on advection Distributions or functions are decomposed into sum of radial basi

s functions (RBFs). Algorithm finds pair of RBFs and compute their mass transport to

obtain the interpolated function.

1

1. Displacement Interpolation Using Lagrandian Mass Transport Displacement Interpolation

If we know the distribution function, displacement interpolation is easy to be calculated. But how we could interpolate distributions whose parameterized formula is unknown?

1. Displacement Interpolation Using Lagrandian Mass Transport Algorithm

Refer to the Paper Video

1. Displacement Interpolation Using Lagrandian Mass Transport Results and Applications

Also Refer to the Paper Video

1. Displacement Interpolation Using Lagrandian Mass Transport Conclusion

Displacement interpolation is useful in many CG situations rather than linear interpolation.

General approach of displacement interpolation on multi-dimensional and continuous domain is proposed.

Several applications of displacement interpolation is demonstrated.

Adaptive Sampling and Reconstruction using Greedy Error Minimization

Abstract2

A new adaptive sampling method for Monte Carlo rendering. It operates in image space and uses iterative approach. It focuses on minimizing the MSE on same # of samples.

2. Adaptive Sampling and Reconstruction using Greedy Error Minimization

Algorithm Overview

Monte Carlorenderer

Rendered image Per-pixel Filters Output Image

Filter SelectionAlgorithm

Sample DistributionAlgorithm

New samples

Note: each algorithm gives solution which minimize the estimated MSE on output image.

2. Adaptive Sampling and Reconstruction using Greedy Error Minimization

Result

2. Adaptive Sampling and Reconstruction using Greedy Error Minimization

Conclusions

An adaptive sampling and reconstruction algorithm that greedily minimize MSE in Monte Carlo rendering is described.

It significantly improves MSE and image quality over previous work.

A robust filter selection algorithm.

T&I Engine: Traversal and Intersection Engine for Hardware Accelerated Ray Tracing

Abstract

A new ray tracing hardware architecture is proposed

It integrates 3 novel architectures;1. cache-efficient layout and

its traversal2. 3-phase ray-triangle

intersection test 3. latency hiding defined as

the ray accumulation unit

3

Paper is not available

Coherent Parallel Hashing

Abstract4

A new spatial hashing algorithm for parallel GPU processing. High load factor and quick rejection time for empty key query. Preserves space coherence so that adjacent data is kept together in

hash table.

4. Coherent Parallel Hashing

Algorithm

Based on Robin Hood hashing [Celis 1986]. Store maximum age of key with each entry in ha

sh table for quick rejection of empty key. Probe function is defined as follows to keep spa

tial coherence.

4. Coherent Parallel Hashing

Examples (Texture Painting)

Atras:4096x4096 1M pixels are painted. Hash construction time and rendering frame rate are 3ms and 446fps for d=0.5, 10ms and 237fps for d=0.99.

4. Coherent Parallel Hashing

Conclusions

A new spatial hashing method is introduced. It can be constructed by GPU in parallel. It keeps high load factor and fast query time (nature

of Cuckoo hashing [Pagh and Rodler 2004]). Empty key queries are quickly rejected. Spatial coherence is maintained. Implemented on nVIDIA Fermi GTX480 (CUDA) It showed remarkable performance.