It tracks the ball throughout the rally and detects racket hits and court bounces. The aim is to eventually turn full match recordings into stats and highlights automatically.

It’s still a work in progress, but I’m pretty happy with how it’s performing so far. I’d love to hear any feedback or ideas for what I should add next.

I’m also looking to test it on more matches, particularly footage where the full court is visible through a wide-angle or fisheye camera—so feel free to message me if you happen to have any.

Hi everyone,

A house in my area was deliberately set on fire, and I have several videos of the incident. One of the videos shows a vehicle leaving the scene, but the license plate is too blurry for me to read. I hope one of you can track the number

6 months back me and a friend built this for a client, and I wanted to write up the architecture because the engineering got genuinely interesting, especially the parts that broke.

The idea: you feed the system a base of source videos, it chops them into logical chunks and indexes them by meaning. Then you give it a target video, and it rewrites a new script and reassembles a fresh video by pulling the semantically matching chunks from your base. Whole thing runs through a Telegram bot with a queue.

The pipeline, roughly:

Indexing side: video gets downloaded, cut into logical chunks with FFmpeg + OpenCV (computer vision to detect what's happening on screen), each chunk goes through Gemini embeddings, vectors land in Qdrant. I originally used Firebase for storage and switched to Qdrant later, which turned out to be the right call by a mile.

Generation side:

1. Target video gets transcribed with Whisper v3 large.
2. Transcript goes to Gemini, which analyzes the meaning and writes a new script (could even switch language, english in, spanish out). Script length matched the original within a minute.
3. Script gets voiced through an 11Labs pay-as-you-go provider (way cheaper than native 11Labs at volume).
4. Then the hard part: assembling the actual video from base clips.

The three problems that nearly killed it:

Semantic precision. The model understands roughly what's on screen but can't reliably tell "is this Dota 2 or CS:GO." So instead of picking one clip, we pulled the top 5 matches per sentence and had the model pick the best. Not perfect, but workable.

Clip length. Fixed partly at cut time (nothing shorter than 3s or longer than 15s), but it still crammed too many clips into short spans sometimes. We matched sentence length against clip length to control it.

Repetition, the worst one. The model LOVED grabbing one clip and spamming it half the video, because it scored as the perfect semantic match every time. Fixed it with a hard frequency cap: one clip couldn't repeat more than once per ~5 retrieval passes.

Render time for a 20-min video was about 40-45 min end to end. yt-dlp for downloads (had a funny problem with it I can get into), whole bot ran locally on a server so big file sends weren't an issue.

Honestly the retrieval-by-meaning part was the most fun engineering I'd done in a while. Happy to go deeper on any part. Anyone else built semantic video retrieval? Curious how you handled the "one clip dominating" problem, that one was brutal.

Hi everyone,

I’m attending ECCV 2026, and my university requires an official day-by-day conference schedule for travel approval.

The official website currently lists the conference dates, but the detailed technical program has not been released yet. I understand that the exact release date may not be known, but based on previous ECCV editions, does anyone have a rough idea of when the detailed schedule is usually published?

Thanks in advance!

Hi all,

I am trying to make a custom depth map tracking rig. I was going to use

2 a2A2464-115g5mBAS Basler ACE2 2448 × 2048, 115 fps, from basler with
Basler 25mm, C23-2528-16M, 2/3” or Basler 50mm, C23-5028-16M, 2/3” for lensing so I can resolve fine features of rodents.

I am going to have a baseline of 40mm and everything can be processed offline. I just wanted to know if I am on the right track for getting sub millimeter resolution depth maps.

Thanks in advance

Title

Best workflow for longitudinal rigid registration and landmark tracking on serial STL meshes?

Hello everyone,

I am a periodontist looking for advice on the most practical computer-vision workflow for analysing serial 3D dental surface scans.

We have five STL meshes of the same patient’s lower dental arch:

• T0: before treatment
• T1: immediately after treatment
• T2: 1 month
• T3: 3 months
• T4: 6 months

The region of interest is the six lower front teeth. These teeth were connected with a wire-composite splint, and we want to measure whether their positions change over time.

A simple analogy is a fence. Imagine six central fence boards whose movement we want to measure, while several boards farther to the sides are assumed to remain stable. Each new scan must first be aligned using only the stable side boards. The six central boards must not influence the registration, because their movement is the outcome of interest.

In our case:

• the six anterior teeth are the moving fence pieces;
• selected posterior tooth surfaces are the stable registration reference;
• the rigid transform calculated from the posterior surfaces should then be applied to the complete follow-up mesh.

The anterior teeth must also be excluded because the splint is added after T0, changing their surface geometry independently of any actual tooth movement.

We will attach two screenshots. The first shows one predefined measurement landmark on each anterior tooth and the posterior reference regions. The second shows a fixed baseline curve passing through the six T0 landmarks. The landmarks, reference regions and curve are already defined; we are seeking advice only on how to implement the analysis.

The required workflow is:

1. rigidly register each follow-up STL to T0 using only the selected posterior surfaces, with rotation and translation but no scaling;
2. apply the resulting transform to the complete follow-up mesh;
3. record the X, Y and Z coordinates of the six predefined landmarks at every timepoint in a fixed coordinate system;
4. calculate ΔX, ΔY, ΔZ and total 3D displacement relative to T0;
5. calculate the shortest 3D distance from each follow-up landmark to the fixed T0 curve;
6. export the results to CSV for statistical analysis.

Ideally, we would also export the transformation matrix and a registration-error measure calculated over the posterior reference surfaces.

We have already tested ICP-based alignment in Medit Design and a custom Open3D application, but we do not yet have a complete and reproducible workflow for registration, landmark collection, point-to-curve distance calculation and data export.

Our main questions are:

1. What software stack or workflow would you recommend for this task?
2. Would Open3D/Python be a sensible approach, or is there an existing tool that already handles most of this reliably?
3. How would you structure the pipeline so that it can later be repeated consistently across multiple patients?

We can provide one fully de-identified example dataset if someone is willing to assess or demonstrate a possible workflow.

Thank you for any practical suggestions, example code or references to similar projects.

Hi Guys,

So I have been working on document layout analysis for some time now. I have tried the models like Doclayout, Docling, Miner U, marker.

Overall Docling performs well, but the problem is that it over performs. And mineru u misses some content like the corresponding author on the page-footer. And it is also missing the masthead mark, and the article-type label.

In my opinion unlimited OCR performs well in all the tasks, but in general it is failing to recognise any style at all. And it is bad at recognising logos.

So I am wondering are there any state of the art models (SOTA) that are good at PDF text extraction and layout extraction ?

Thanks

Hey,

I'm building a photosphere rig - for that I was looking into good industrial cameras, since they need to meet the following criteria:

• Global Shutter
• At least 20MP (Most seem to cap out at 24MP)
• At least 10FPS
• Since this will be build in a cluster of multiple cameras, time sync, so they all fire at the same time
• Color (they will be used outdoors in varying lighting condition)
• Need software control over things like ISO, Exposure etc.
• Preferably connected via Ethernet/Coax (not USB)
• Good API to interact with them via C/Rust

Brands I looked into (they are very expensive tho)

• LUCID Vision Labs Triton2
• Compare Basler ace 2 a2A5060-35cc
• Teledyne Blackfly S USB3: 24,5 MP, Farbe, C-Mount
• Alvium G5-2460 Color

I will require ultrawide lenses on the cameras So to cover around 100-100 degrees FOV.

Does anyone have experience with these brands/models? Are there even high res options available? Any cheaper alternatives, perhaps some Chinese brands etc.

I have no experience with image processing and I am trying to vibe code a tool to replace backgrounds in used-car listing photos for a family member who owns one dealership.

Two requirements: (1) the car exterior and interior must stay pixel-identical — no regeneration or distortion, and (2) background visible through windows needs replacing too.

Generative models (GPT-image-2, Nano Banana) solve the window problem but subtly alter the car — paint tone, reflections, distorted text on plates/displays, occasional distortion on unusual angles.

Segmentation models (SAM2, BiRefNet) preserve the car
perfectly but treat glass as solid — they don't flag the
background bleeding through windshields/rear windows as background at all.

Has anyone solved this specific combination? Preferably with API access which I can incorporate into the workflow.
Background image is also provided as input.

Hey all ! Is anyone else going to be at KDD in Jeju? Would love to connect with fellow attendees.

I work on interpretability, fairness, and editing of text-to-image models, so I'd especially love to meet people working in these areas. But honestly, we can chat about anything: research, the conference, life, or grab a coffee/drink.

I land in Jeju on the night of the 8th of August, so hmu if you're around and want to link up!

I’m trying to train an object detection model for a specific object. I trained it on a custom dataset collected which focused on outdoor and indoor environments and mostly LED light. But as soon as I run the pipeline on inference (edge device) in a completely different environment (indoor and fluorescent light) the detection does not work efficiently which makes sense as the dataset did not contain that specific scenario. But let’s say if I want to run this for a commercial product which could consist of different lighting and environment settings, what could I do in the training pipeline or dataset to make the detector generalized to all different settings?

Would appreciate any advice for people working in this field with edge device constraints.

Tried template matching. Tried segmentation SAM2

Thin structures are where dense vision systems usually give themselves away. A cable gets swallowed by the background. Two nearby objects turn into one blob. Good average segmentation numbers do not say much about those cases.

LingBot-Vision uses masked boundary modeling in its public release and provides several ViT/16 backbones, including a roughly 1.1B version. The basic question is whether those features keep the boundaries when a small decoder is attached. Test a frozen or lightly tuned backbone on boundary errors, depth breaks, and small objects. If it only wins on a PCA plot, that is a much smaller result.

I’m really confused, guys, and I don’t know what I should do.

I have a Bachelor's degree in Computer Science, and now I have to choose between Data & AI or Cloud & Internet of Things (IoT).

I like networking to some extent, but I’m worried I won’t be able to handle Cloud because it seems to require a lot of automation and DevOps skills. On the other hand, one of my professors recommended that I choose Data & AI, but I’m also a bit scared because I’m not a big fan of software development.

Another thing is that if I get an opportunity to continue my studies in Europe next year, I’ll definitely take it, so I’m considering this master's year mainly as a way to build my skills and strengthen my background.

I also have an opportunity to study in Italy, but I’m not sure if Italy is the best choice for tech compared to some other European countries.

What would you do if you were in my situation? Any advice or personal experiences would really help. Thanks! 🙏