RRepoGEO

REPOGEO REPORT · LITE

google-deepmind/tapnet

Default branch main · commit b5f3a616 · scanned 6/29/2026, 12:27:50 PM

GitHub: 1,928 stars · 184 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface google-deepmind/tapnet, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify `tapnet`'s role as the primary repository for TAPIR and related projects in the README

    Why:

    CURRENT
    Welcome to the official Google Deepmind repository for Tracking Any Point (TAP), home of the TAP-Vid and TAPVid-3D Datasets, our top-performing TAPIR model, and our RoboTAP extension.
    COPY-PASTE FIX
    Welcome to the official Google Deepmind repository for Tracking Any Point (TAP). This repository serves as the central hub for the TAPIR model, the TAP-Vid and TAPVid-3D Datasets, and the RoboTAP extension, providing a comprehensive suite for robust point tracking in videos.
  • mediumreadme#2
    Add a dedicated section or prominent statement on `tapnet`'s robotics applications

    Why:

    COPY-PASTE FIX
    ## Robotics Applications

TAPIR's robust point tracking capabilities, particularly through the RoboTAP system, are highly effective for real-world robotics manipulation tasks. RoboTAP enables efficient imitation learning by utilizing precise point tracks, making `tapnet` a valuable tool for roboticists seeking advanced computer vision for control and interaction.
  • lowtopics#3
    Add more specific topics related to video and arbitrary point tracking

    Why:

    CURRENT
    benchmark, computer-vision, deep-learning, point-tracking, robotics
    COPY-PASTE FIX
    benchmark, computer-vision, deep-learning, point-tracking, video-tracking, arbitrary-points, robotics

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface google-deepmind/tapnet
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RAFT
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. RAFT · recommended 1×
  2. GMA · recommended 1×
  3. TAPIR · recommended 1×
  4. CoTracker · recommended 1×
  5. DeepLabCut · recommended 1×
  • CATEGORY QUERY
    What deep learning techniques are effective for tracking arbitrary points across video frames?
    you: not recommended
    AI recommended (in order):
    1. RAFT
    2. GMA
    3. TAPIR
    4. CoTracker
    5. DeepLabCut
    6. DeepFlow
    7. Farneback Optical Flow
    8. SuperPoint
    9. SuperGlue

    AI recommended 9 alternatives but never named google-deepmind/tapnet. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a robust computer vision system to track points for robot manipulation tasks.
    you: not recommended
    AI recommended (in order):
    1. OpenCV (opencv/opencv)
    2. AprilTags (AprilRobotics/apriltag)
    3. Intel RealSense SDK (IntelRealSense/librealsense)
    4. OpenPose (CMU-Perceptual-Computing-Lab/openpose)
    5. Photoneo PhoXi 3D Scanners
    6. ROS (ros/ros)
    7. MoveIt! (ros-planning/moveit)
    8. PCL (PointCloudLibrary/pcl)
    9. ZED SDK

    AI recommended 9 alternatives but never named google-deepmind/tapnet. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of google-deepmind/tapnet?
    pass
    AI named google-deepmind/tapnet explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts google-deepmind/tapnet in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google-deepmind/tapnet explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo google-deepmind/tapnet solve, and who is the primary audience?
    pass
    AI named google-deepmind/tapnet explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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google-deepmind/tapnet — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite