3D Object Representations for Fine-Grained Categorization Jonathan - PowerPoint PPT Presentation

3D Object Representations for Fine-Grained Categorization Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei

What is this?

What is this? Car

What is this? Sedan

What is this? BMW Sedan

What is this? BMW 3-Series Sedan

What is this? 2013 BMW 3-Series Sedan

What is this? 2013 BMW 3-Series Sedan 328i

Difficulty How many classes are there?

Difficulty How many classes are there?

Why 3D?

Why 3D?

Related Work • Many works on fine-grained recognition and 3D recognition • Birdlets Birdlets – 3D volumetric bird model – Pose normalization – Extensive training annotations Birdlets: Subordinate Categorization Using Volumetric Primitives and Pose-Normalized Appearance. R. Farrell, O. Oza, N. Zhang, V. I. Morariu, T. Darrell, L. S. Davis. ICCV 2011

Method Overview 1. Estimate 3D geometry 2. Calculate appearance w.r.t. geometry 3. Use appearance in 3D representation

Getting 3D Geometry • Train geometry classifier from synthetic data – Generate synthetic data from CAD models – Group synthetic data by azimuth, elevation, and coarse type • sedan, coupe, convertible, SUV, pickup, hatchback, station wagon station wagon – SVM • At test time use multiple hypotheses Base HOG features Learned classifier

Synthetic Data • 41 CAD models • 36 azimuths • 4 elevations • 10 backgrounds 10 backgrounds • 59,040 synthetic images w/full 3D annotations

Appearance • Sample patches directly from 3D surface • Rectify patches for viewpoint invariance

3D Representation 1: SPM-3D • Extension of Spatial Pyramid Matching to 3D 1. Compute features for each patch 2. Pool over regions on object surface We use 1x1,2x2,4x4 pooling regions We use 1x1,2x2,4x4 pooling regions Beyond Bags of Features: Spatial Pyramid Matching for recognizing natural scene categories. S. Lazebnik, C. Schmid, J. Ponce. CVPR 2006

3D Representation 2: BB-3D • 3D version of randomized BubbleBank [Deng et al. CVPR 2013] • BB-2D: random templates + local pooling regions Fine-Grained Crowdsourcing for Fine-Grained Recognition. J. Deng, J. Krause, L. Fei-Fei. CVPR 2013

BubbleBank-3D 1. Randomly sample templates 2. Pool over local 3D region

Fine-Grained Car Datasets • Existing datasets are small and not very fine-grained – car-types : 14 classes, variety of coarse categories • Two new datasets: – BMW-10: Ten classes, ultra-fine-grained BMW-10: Ten classes, ultra-fine-grained – car-197: 197 classes, much bigger • In terms of # images: car-types car-197 Fine-Grained Categorization for Scene Understanding. M Stark, J. Krause, B. Pepik, D. Meger, J.J. Little, B. Schiele, D. Koller. BMVC 2012

BMW-10 • 10 types of BMWs, 512 images, many viewpoints, bounding boxes, hand-curated

Car-197 • 197 car models, 16,185 images • Collected very carefully on AMT • Slightly modified version in FGComp • Standalone dataset out soon Fine-Grained Challenge 2013. http://sites.google.com/site/fgcomp2013

Experiments: BMW-10 70 60 50 40 ccuracy Accur 30 30 20 10 0 3D works!

BB-3D: Local vs. Global • BB-3D-L: 64.7%, BB-3D-G: 66.1% • Why global pooling can work: – More robust w.r.t. difficult viewpoints – Left-right symmetry Left-right symmetry

Experiments: car-types 100 95 90 ccuracy 85 Accu 80 75 70 Still works!

Experiments: car-197 78 76 74 72 70 Accuracy 68 66 64 62 62 60 58 56 LLC+SPM SPM-3D BB BB-3D-G Stacked • The problems: – Underrepresentation of some types of CAD models – Template vs. codebook approaches with many classes • The silver lining: Stacking helps a lot :)

Discriminative Bubbles Discriminative power of templates in BB-3D (BMW-10): Size/color proportional to Discriminative features at front/back!

Bonus: Ultra-Wide Baseline Matching • Measures ability to localize 3D points across viewpoints • Use BB-3D-L + RANSAC for correspondences

Experiments: Ultra-Wide Baseline Matching • On 3D Object Classes BB-3D-S: Single geometry hypothesis BB-3D-M: Multiple geometry hypotheses • Works well, state of the art for some baselines 3D Generic Object Categorization, Localization, and Pose Estimation. S. Savarese, L. Fei-Fei. ICCV 2007 [24] 3D 2 PM – 3D Deformable Part Models. B. Pepik, P. Gehler, M. Stark, B. Schiele. ECCV 2012 [37] Revisiting 3D Geometric Models for Accurate Object Shape and Pose. M. Z. Zia, M. Stark, B. Schiele, M. Schindler. 3DRR 2011

But Wait, There’s More: Reconstruction of Category • Same fine-grained category, different instances, backgrounds, lighting, etc. • Pipeline: BB-3D-L for point correspondences→ VisualSFM for bundle adjustment VisualSFM for bundle adjustment

Conclusion • Lifted two representations to 3D (SPM-3D, BB- 3D) which are state of the art on two fine- grained datasets • Two new fine-grained datasets of cars • Two new fine-grained datasets of cars • Promising initial results on ultra-wide baseline matching and reconstruction of a fine-grained category

Thank You!