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SIH Buddyby Ganeev Singh
Dev

🔥 Roast My Pick · SIH26166

Multi-modal, Sun angle and scale invariant image correspondence using Chandrayaan-2 optical images (OHRC, TMC and IIRS)

Indian Space Research Organisation(ISRO)

Mild12/100

Good pick. Genuinely. Now sit down, because the judges are going to try anyway — and this is what they will try.

Strong pick. Well-specified with public data and named metrics, and learned matchers give a real path — build for the sun-angle and cross-sensor cases specifically, because those are where the sub-pixel bar bites and where an ISRO judge will look first. Roughly 90–210 teams are expected to go here.

The receipts

Every red flag on this statement, in full. These are the four places it bites.

  1. Exhibit A

    Sub-pixel accuracy under extreme sun-angle change is a hard bar, and classical and even learned matchers degrade exactly where illumination differs most

  2. It gets worse

    Cross-sensor matching between optical and hyperspectral IIRS imagery is the hardest case and the one most likely to be tested

  3. Still reading?

    The uniform-distribution requirement rules out solutions that only match the few high-texture regions, which is where matchers naturally concentrate

  4. And the finisher

    An ISRO judge works with this data professionally and will probe the failure cases a demo on easy pairs would hide

The damage report

Every score this statement earned, and what each one actually costs you.

  • Feasibility

    3/5

    Buildable. Not comfortably. There is a week in here you have not planned for yet.

    Chandrayaan-2 imagery is publicly available through ISRO's data portal and learned feature matchers like SuperGlue and LoFTR handle exactly this kind of illumination and viewpoint invariance, but sub-pixel accuracy on the lunar surface under extreme sun-angle change is genuinely hard, and cross-sensor matching between an optical camera and a hyperspectral imager is harder still.

  • Innovation scope

    4/5

    There is something genuinely new here. Do not bury it under another dashboard.

    Making correspondence robust simultaneously to illumination, viewpoint, scale and sensor modality on lunar terrain, with uniform match distribution and sub-pixel accuracy, is genuinely open — off-the-shelf matchers do not solve the sun-angle and cross-sensor cases well.

  • Clarity

    5/5

    The ask is unambiguous, which quietly removes your favourite excuse.

    The description defines the registration components, names the three challenge types precisely, specifies the payloads, states the sub-pixel accuracy and uniform-distribution requirement, and names the evaluation metrics, making it exceptionally well-specified.

  • Acceptance potential

    4/5

    Strong footing before you have written a line. Try not to waste it.

    A strong pick — the data is public, the requirement is precise with named metrics, learned matchers give you a real path, and the lunar-imaging domain thins the field, though sub-pixel accuracy under extreme illumination is a genuine bar and cross-sensor matching is the hard case a knowledgeable ISRO judge will test.

  • Effort

    Heavy

    Heavy. Somebody on this team is not sleeping in week three. Pick who, on purpose.

    Building and adapting a robust matcher to lunar imagery, handling cross-sensor and multi-scale cases, and validating to sub-pixel accuracy is focused, demanding computer-vision work.

  • Demo-ability

    Medium

    Demoable, if you rehearse it. Nobody rehearses it.

    A registration that succeeds where classical matching fails is a clear result, but it is quantitative — RMSE and inlier ratios — rather than visually dramatic, so the improvement needs the metrics to land.

The demo they will have already seen

Somewhere around 90–210 teams are heading here, and the description is doing the choosing for most of them. They will read the same brief, reach the same architecture, and build a version of the same demo you are planning. Being correct is the floor. If your five minutes could be swapped with the team before you and nobody in the room would notice, you have not picked badly — you have built predictably, which costs exactly the same and hurts more.

What survives

The ground worth standing on when the questions start.

  • Chandrayaan-2 imagery is publicly available through ISRO's data portal, so you can develop on the real target data
  • Learned matchers like LoFTR are built for the illumination and viewpoint invariance this problem needs, giving you a genuine head start
  • The named metrics — RMSE, inlier ratio — mean your evaluation is objective and pre-defined rather than argued

Nothing here is fatal. It is just the list of places this statement pushes back, and you now get to push there first.

Full analysis ↗Roast another

The framing is a joke. The findings are not — they are the same analysis on the statement page, and every line above is attached to a score or a fact in the record. It is one opinion with its reasoning attached, so argue with it before you trust it.