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© 2012 Steve Marschner • Cornell CS6630 Spring 2012 Bidirectional Path Tracing Images for CS6630 lecture

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Page 1: Bidirectional Path Tracing - Cornell University · 10.5. RESULTS 325 Figure 10.4: This figures shows the weighted contribution that each bidirectional sampling technique makes to

© 2012 Steve Marschner • Cornell CS6630 Spring 2012

Bidirectional Path Tracing

Images for CS6630 lecture

Page 2: Bidirectional Path Tracing - Cornell University · 10.5. RESULTS 325 Figure 10.4: This figures shows the weighted contribution that each bidirectional sampling technique makes to

10.5. RESULTS 325

Figure 10.4: This figures shows the weighted contribution that each bidirectional samplingtechnique makes to Figure 10.3(a). Each row shows the contributions of the samplingtechniques for a particular path length . The position of each image in its rowindicates how the paths were generated: the -th image from the left in each row useslight subpath vertices, while the -th image from the right uses eye subpath vertices. (Forexample, the top right image uses light vertices and eye vertex, while the bottomleft image uses light vertex and eye vertices.) Note that these images have beenover-exposed so that their details can be seen; specifically, the images in row were over-exposed by f-stops. The images were made by simply recording the contributions ina different image for each value of and .

light segments → ← eye segments

0 011

0

112 02

3 1 2 2 1 3 0

0

0 1 2 3 43 2 1 04

Eric Veach

Page 3: Bidirectional Path Tracing - Cornell University · 10.5. RESULTS 325 Figure 10.4: This figures shows the weighted contribution that each bidirectional sampling technique makes to

Wenzel Jakob

Page 4: Bidirectional Path Tracing - Cornell University · 10.5. RESULTS 325 Figure 10.4: This figures shows the weighted contribution that each bidirectional sampling technique makes to

Wenzel Jakob