PanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding
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1 PanoContext: A Whole-room 3D Context Model for Panoramic Scene Understanding Yinda Zhang Shuran Song Ping Tan Jianxiong Xiao Princeton University Simon Fraser University Alicia Clark PanoContext October 17, / 11
2 Importance of Context in Image Processing Alicia Clark PanoContext October 17, / 11
3 Importance of Context in Image Processing Alicia Clark PanoContext October 17, / 11
4 Importance of Context in Image Processing Alicia Clark PanoContext October 17, / 11
5 Importance of Context in Image Processing Alicia Clark PanoContext October 17, / 11
6 Importance of Context in Image Processing Context Models Proposed in the past, but they do not work as well as expected WHY? Alicia Clark PanoContext October 17, / 11
7 Field of View (FOV) Small FOV = Hinders the contextual information Alicia Clark PanoContext October 17, / 11
8 Field of View (FOV) Alicia Clark PanoContext October 17, / 11
9 Field of View (FOV) Alicia Clark PanoContext October 17, / 11
10 Panorama Horizontal and 180 Vertical Easily obtained by smartphones, special lenses, or image stitching All objects are usually visible despite occlusion Enables the detection of the room layout and of the contextual information Panorama = Object Detection = Whole-room 3D Context Model Alicia Clark PanoContext October 17, / 11
11 Panorama Horizontal and 180 Vertical Easily obtained by smartphones, special lenses, or image stitching All objects are usually visible despite occlusion Enables the detection of the room layout and of the contextual information Panorama = Object Detection = Whole-room 3D Context Model 1 Manhattan world assumption: assumes the scene consists of 3D cuboids aligned with the three principle directions 2 Assume no floating objects Alicia Clark PanoContext October 17, / 11
12 PanoContext: A Whole-Room 3D Context Model Algorithm 1 Generate a set of hypotheses for room layout and objects 2 Rank these whole-room hypotheses holistically to determine the best hypothesis Alicia Clark PanoContext October 17, / 11
13 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
14 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Figure : Hough transform for vanishing point detection Alicia Clark PanoContext October 17, / 11
15 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
16 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Figure : Comparison of OM and GC. OM works better on the top half of the image, while GC provides better normal estimation at the bottom Alicia Clark PanoContext October 17, / 11
17 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
18 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
19 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
20 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
21 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Figure : Two ways to generate object hypotheses Alicia Clark PanoContext October 17, / 11
22 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
23 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
24 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses 1 Semantic Label Alicia Clark PanoContext October 17, / 11
25 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
26 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses 1 Pairwise Constraint Alicia Clark PanoContext October 17, / 11
27 PanoContext: A Whole-Room 3D Context Model 1. Generate a set of whole-room hypotheses Alicia Clark PanoContext October 17, / 11
28 PanoContext: A Whole-Room 3D Context Model 2. Determine the best whole-room hypotheses Train a linear SVM model to rank the whole-room hypotheses and choose the best hypothesis Want the matching cost (difference between whole-room hypothesis and its ground truth) to be as low as possible Alicia Clark PanoContext October 17, / 11
29 Summary: How does context help? Helps to determine the size of objects Alicia Clark PanoContext October 17, / 11
30 Summary: How does context help? Helps to determine the size of objects Helps to determine the correct number of objects Alicia Clark PanoContext October 17, / 11
31 Summary: How does context help? Helps to determine the size of objects Helps to determine the correct number of objects Helps the determine the relative position of objects Alicia Clark PanoContext October 17, / 11
32 Summary: How does context help? 1 DPM: Deformable Part Model 2 Felzenszwalb et. al: Discriminative training with partially labeled data Alicia Clark PanoContext October 17, / 11
33 Contributions Context model is fully in 3D First annotated panorama dataset Alicia Clark PanoContext October 17, / 11
34 Questions? Alicia Clark PanoContext October 17, / 11
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