Final Report & Presentation Expectations

Sumant Sharma, CS 231a CA May 27, 2016

Agenda

• Presentation Logistics

• Presentation Content

• Final Report Content

Presentation Logistics• Sign up for a time period before 11:59PM, May 29. Google Doc link

• Slot 1: 3:00PM to 4:20PM in Skilling Auditorium

• Slot 2: 3:00PM to 4:20PM in 260-113

• Slot 3: 7:00PM to 9:00PM in 380-380Y

• Slot 4: 7:00PM to 9:00PM in 420-041

• SCPD students need not sign up. Upload to YouTube and send link to cs231a-spr1516-staff@lists.stanford.edu by 9:00 PM, June 1

• If possible, select the slot where the CA who graded your milestone is in attendance. Pizza and soda provided for Slot 3 and Slot 4

Presentation Logistics• Your are expected to attend all presentations in your slot and ask

meaningful questions (remember: class participation grade!)

• You are expected to line-up before the previous presentation ends, zero credit if you miss your turn!

• For students presenting in one of the four slots: your presentation slides need to be in your slot-specific Google Slides document. The link to your particular Google Slides document will be available on May 30 here: Google Doc link and on Piazza

• Slides are due in your document by 11:59PM, May 31, without exception!

Presentation Content• Should cover:

• Problem definition, motivation, and previous work

• Technical description of your solution

• Experimental setup & Preliminary Results

• Presentation may be no longer than 4 minutes; 30 seconds for Q&A

• Presentation supposed to have preliminary results, final results can be included in the report

Presentation Content• Novel algorithm/application/aspect or contribution to

the state-of-the-art will be graded favorably.

• If replicating results of a paper, at minimum, a majority of the original paper must be implemented by yourself.

• Implementing machine-learning algorithms alone is not sufficient.

• If an off-the-shelf algorithm is being used, instead of focusing on implementing the architecture yourself, focus on extensive experimentation.

Final Report Logistics

• Submit report as a PDF on Gradescope

• Submit code to cs231a-spr1516-staff@lists.stanford.edu

• Due on June 6, 11:59 PM - no exceptions/extensions!

Final Report Content• Abstract: Concise summary of what your project is about.

200-300 words. For example, mention the general topic area, what is the novelty aspect, how it relates to the literature

• Introduction: Provide a concise statement of the problem you’re tackling and the solution you’re implementing. Discuss the scope of the technical work (sub component development vs. exhaustive experimental validation of existing algorithms). Discuss how it relates to the literature. Provide a brief outline of the paper’s content and sections.

• Problem Statement: Basic mathematics of the work, coordinate frames through figure(s)

Final Report Content• Technical Content: Section (or multiple sections) describing the

sub-components of the solution approach (or all the approaches you implemented)

• Experimental Setup & Results: How did you test the algorithms you developed? Think of the different test cases, datasets, hardware (if any), metrics used

• Conclusions: Do not just summarize the results, this is an okay start but you also need to highlight the key insights you learned, make conclusive statements about the issues you faced or any present caveats you became aware of in your solution approach

• Formatting: Use CVPR 2016 LaTex/Word template

What do we do now?!• Review the best project reports on the website

• Come talk to the TA’s! Note the extra office hours next week:

• Monday 9am-11am: Kratarth

• Tuesday 9am-11am: Kenji

• Thursday 4pm-6pm: Lyne

• Thursday 8pm-10pm: Sumant (SCPD thru Zoom)

Appendix

• Example presentation

Lost in SpaceComputer Vision for Surface Mapping of Asteroids

A sh ley C la r k · A di tya M a ha j a n · Sum a nt Sha r m a

Background• The problem

• Space missions require increasing autonomy• SLAM: Loop closure difficult due to motion

uncertainty

A. CLARK, A. MAHAJAN, S. SHARMA 2

• Current approaches• Vision-based mapping (sensitive to lighting)• Point cloud matching with ICP

(time consuming, sensitive to initialization)

Our idea: 3D data → 2D techniques

Background• The problem

• Space missions require increasing autonomy• SLAM: Loop closure difficult due to motion

uncertainty

A. CLARK, A. MAHAJAN, S. SHARMA 3

• Current approaches• Vision-based mapping (sensitive to lighting)• Point cloud matching with ICP

(time consuming, sensitive to initialization)

Image Generation• Grayscale image generation (similar to DEM)

• Select a swath of point cloud data, 𝑝 ∈ ℝ3

• Calculate average surface normal, 𝑛• Project readings onto 𝑛, and scale the values: 𝑝 ⋅ 𝑛 → 0,1

A. CLARK, A. MAHAJAN, S. SHARMA 4

Image Matching• Image matching algorithms

• RANSAC • Classic approach• Reduce search with K-means

• Histograms• K-means clusters• Hierarchical k-means clusters

• Image feature types• SIFT• Zernike

A. CLARK, A. MAHAJAN, S. SHARMA 5

0 200 400 600 8000

0.01

0.02

0.03

0.04Composition of Image Features, Test Image 36

Tree Leaf Node #

Norm

alize

d Fe

atur

e Co

unt (

%)

ResultsSIFT & K-means SIFT &

Hierarchical K-means

A. CLARK, A. MAHAJAN, S. SHARMA 6

SIFT & RANSAC

Conclusions• 2D image processing can be used to match 3D point clouds

• Machine learning can reduce the search space• K-means clustering• Hierarchical K-means

A. CLARK, A. MAHAJAN, S. SHARMA 7

Future Work • Comparison of Zernike vs. SIFT features

• Modification of RANSAC to leverage K-means clustering

Questions?

A. CLARK, A. MAHAJAN, S. SHARMA