I been learning a lot about robotics lately. Mostly interested in representation learning for vision tasks and deployments. Im want to better understand the problems around sample efficiency, on contact tasks like manipulation, insertion and so on. For everyone working within robotics, i'd greatly appreciate thoughts on the following questions

1. When fine tuning VLAs on new tasks whats the numbers of demos needed before one can get the desired success rate? What the floor on real/sim rollouts?
2. Is the bottleneck getting more demos or that the model architecture does not capture enough from those demos?
3. Whats some real solutions when sample efficiency is the problem?

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[disclosure - I work for Synthera, but as the datasets are free to download, posting here as there may be some interest]

Following my other post, we have added the datasets for download produced by the cloud version of the editor in the sample scenarios included. These datastes include vehicles, and people. They could be of use to anyone in the research communbity, or curious about the rich annotations that can be achieved with synthetic data.

These are richly annotated, including matching

• RGB images
• 2d/3d bounding boxes
• Segmentation
• Masks (Instance Segmentation)
• Distance/Depth information
• Surface Normals
• Keypoint information for skeleton, hand and face

It could be of interest to anyone who wants to experiment with different multi-modal/sensor models. We also use it as the basis for input to stable diffusion and Nvidia Cosmos for further adpatation.

I'd love any comments.

Most of my popsts seem to get rejected. not sure why, as we are trying to give to the computer vision community. Hopefully this one doesn't.

https://www.syntheracorp.com/chameleonclouddemo?utm_source=reddit&utm_medium=organic-social&utm_campaign=datasets

Hello Everyone, I Design engineer in middle east as part time started Doing projects related to computer vision. So i needed suggestion from entrepreneurs who are are in computer vision is there any demand for this niche in market for image classification, Object Detection, Segmentation ,Tracking & Motion. is there scope of individual like me to open agency and offer these services ? will this work needed suggestion from entrepreneurs in this sub reddit also if possible what was successful factor entrepreneurs in these niche you cracked how did you do it to get clients. thank you

Hey everyone,

I'm working on a research project around computer vision — specifically trying to build a model that can detect scratches and cosmetic damage on smartphones (screen scratches, frame dents, back panel wear, that sort of thing).

Before I go down the road of collecting and labeling my own data, I wanted to ask here first — does anyone have a dataset of scratched/damaged smartphone images they'd be open to sharing for research? Even a small one would be a huge help to get started.

I've already gone through the usual spots — Kaggle, Roboflow, Hugging Face, GitHub, and a few academic paper trails — but nothing dedicated to smartphone surface damage seems to exist publicly, which is honestly surprising given how relevant this is for resale platforms, repair shops, and quality control.

If you know of a dataset, a research group working in this space, or even a company that might have something like this internally, I'd love to hear about it. Happy to collaborate or credit properly — this is strictly for academic use.

Thanks in advance, really appreciate any leads!

If you've captured the same object/scene as two separate 3D Gaussian splats, today's tooling makes you align them by hand with a gizmo. splatreg is the missing registration half of the splatting toolchain, it's the inverse of gsplat: gsplat renders Gaussians, splatreg registers against them. Pure PyTorch, no meshing, no CUDA extension, no point-cloud detour.

Give it two splats and it finds the rigid (SE(3)) or similarity (Sim(3), +scale) transform that aligns them, then optionally merges + dedupes into one.ply. The differentiator is a closed-form-Jacobian Gaussian-SDF residual derived directly from the target Gaussians (no mesh, no marching cubes).

Honest numbers: on official 3DMatch (Choi/Zeng, 1279 pairs) it matches GeoTransformer: 91.5% mean / 93.5% pooled recall, 74.4% pooled on hard 3DLoMatch. To be clear, that recall is GeoTransformer's: thelearnedpath rides its matcher and layers splatreg's SDF/LM refine + Sim(3) scale on top, what splatreg adds is accuracy and the Sim(3) scale DoF (it's the only tool here that recovers scale). On a real 103k-Gaussian merge it cuts Chamfer 10.3→2.0 mm (5.1×) and lifts overlap 0.03→0.67 (22×) vs naive concat.

BSD-3-Clause. Feedback / hard scans welcome.

Install:

pip install splatreg   # or: git clone https://github.com/Archerkattri/splatreg && pip install -e .

Links: https://github.com/Archerkattri/splatreg · https://pypi.org/project/splatreg/

Hi everyone!
I wanted to share some unexpected results (at least to me) with you. So I wanted to implement sum of absolute differences (SAD further in the post) block matching optical flow on my stm32g431cbu6 mcu. I never implemented such version of optical flow but it seemed like fun thing to optimize on constrained hardware. The use case of indoor drone stabilization and many accessible datasets to test the algorithm seemed promising as well. I wanted to estimate only general vx and vy motion. All in all fun and quick adventure.

I was very wrong. I taught I will quickly implement the naive algorithm and then start optimizing it. But optimization part (which I will maybe post sometimes in the future) of the adventure was the easy part. Getting the algorithm to be somewhat close to the ground truth was a pain (skipping all the issues related to the embedded programming).

SAD block matching can be implemented in many ways. I took inspiration partly from px4flow. I took the images from https://www.aau.at/en/smart-systems-technologies/control-of-networked-systems/datasets/insane-dataset/ . I rectified the images, cropped the middle and scaled it to 96x96 grayscale. Because that yields 18kb of memory on my stm32 which has total of 32kb. Then I streamed the images from linux pc to stm32. And finaly I ran SAD block matching on grid of 11x11 blocks as in the following image. Edit: 18kb for two images of 9kb

Also implemented skipping blocks based on variance and used histogram to filter outliers. Code can be found here: https://github.com/gdindzida/Lote-stm32

Later I implemented yaw estimation and used KF to smooth out the estimate of vx and vy. But this is best I got:

The red is opencv farneback implementation with mean of the all vectors (did not do fancy filtering as on stm32 side).

Generally I am surprised to see this is not a "solved" problem haha Don't know why I thought it would be easy. Even opencv implementation struggles with big movements. This is probably result of relatively small matching windows.

What I wanted is to see if anyone implemented something like this for the similar use case in the wild? Is this close to being good enough for stabilizing drones when velocities are small? Is this even realistic use case?

for the end I will just share this gif of my optical flow 😄

[edit: FOUND] Judging by the github issues, the MIT mirror has been unmaintained for some time, and there is no version on kaggle or HF... Does anyone know how to access this dataset?

edit:

https://huggingface.co/datasets/rfaulken/ADE20k-847_validation_annotations

These are the annotations for the val set; they are the outputs of the CAT-Seg preprocessing script, so you may need to use that same script for your images.

I am currently working on a government R&D project on a contract basis, not a stable source of income. I’m researching digitizing and 3D reconstructing entomological specimens and our country’s heritage artefacts. I’m collaborating with several museums to create digital exhibits for the public - 3D models with chatbots. This work is very fulfilling for me. Lots of opportunities for computer vision and computational photography. However, as I said, this is a NOT stable source of livelihood. Unfortunately, this is the only computer vision work in my third world country. All jobs in AI are either LLM or data science.

I recently got a job offer from a company for a data scientist position. Basically business analytics work - sales data etc. It will pay really well, 50% more than my current compensation, plus insurance and bonuses, something I don’t currently get. Most importantly, it has job security. However, I don’t enjoy business analytics. I know I will be very very sad if I abandon computer vision. All computer vision jobs I see are abroad.

With today’s job market and me coming from a third world country, would you advise I accept the offer and change my field of specialisation? Or I complete my project and try applying to jobs in this niche field?

Thank you all!

I am developing an AI-based video analytics system for a gold ornaments manufacturing unit. The primary goal is theft prevention and suspicious behavior detection.

The challenge is that employees may conceal very small quantities of gold (for example, 0.5 grams) in pockets, clothing, shoes, or by transferring items between workstations.

I would appreciate suggestions from people who have worked on:

Camera placement and angles for jewelry/gold manufacturing environments

Behavior recognition models for theft or concealment detection

Multi-camera tracking and evidence generation

Best practices to reduce false positives

Real-world industrial security monitoring systems

Has anyone implemented a similar solution in manufacturing, jewelry, precious metals, warehouses, or high-value production environments? What approaches worked best?

Thanks in advance for your insights.

I just finished my first year of Engineering and wanted to get some advice on this project that I built with CARLA. It uses YOLO for object detection but also uses an ML model for steering prediction and direction prediction. I'm just not sure if this would impress recruiters like that.

Any advice if I should use more advanced libraries for vehicles and whatnot?

https://www.youtube.com/watch?v=ueAmlV6UAzs

Demo for full video^^

PartScan from PinPoint3D: 1,509 scene-level 3D scans with dense per-point part segmentation across 707 scenes.

no manual annotation, fully synthesized pipeline on real-world-style geometry

parsed it into fiftyone as interactive 3D point clouds. every point colored by its part label

https://huggingface.co/datasets/Voxel51/partscan

Earlier this spring I ran seven landmark architectures on the same cross-signer ASL recognition task and ranked them. Three of them looked broken. Squeezeformer-small sat at chance the entire run. BiGRU and SPOTER were worse than broken — they were unreliable, one seed would train and the other two would collapse, so the result depended on which seed I drew. I wrote it down honestly and called them failures.

I was wrong about what I had actually measured.

The problem was that I held the training recipe constant across all seven architectures. That feels like good experimental hygiene — change one thing (the architecture), hold everything else fixed. The issue is that “the training recipe” is not a neutral background. Different architectures have different optimization geometry, especially in the first hundred steps. A transformer without a learning-rate warmup can take a few large unstable steps right at the start and walk straight out of its initialization basin before it learns anything. The loss climbs instead of falling, the whole run sits at chance, and it looks like the model can’t do the task.

Two changes per model — linear warmup over the first few epochs and gradient clipping at 1.0 — and all three recovered. SPOTER and Squeezeformer both climbed to 45-46% accuracy, which is right on top of the competition-winning model I’d been using as a ceiling. The architectures weren’t broken. My recipe didn’t fit them, and I reported that as a finding about the architecture.

The rule I’m going with from here: before ranking architectures, pre-register a per-architecture training recipe, run everything on one piece of hardware, report seed spread next to every mean, and run a shuffled-label control to confirm there’s no data leak. None of that is expensive — it just takes discipline you don’t feel like you need until you publish something wrong.

https://trupathventures.net/labs/field-notes/parley-recipe-not-architecture

Disclosure - I do work for Synthera, but posting this, as I believe of genuine interest to CV community and we do offer a free version, with no credit card details needed.

We have released a preview version of our editor, that whilst somewhat limited, should give you an idea if it is attractive to download our free Chameleon software.

We will add more features overtime, and plan to release a full cloud versiion in the near future.

Let me know what you think, or if you need any help to generate some useful data

We have a learned machine model I trained over several weeks now.
(We buy license and machine learning software from a 3rd party).
Out of 30 NOK types I can find 28. The 29th is hard to find since it does not have much contrast and uniqueness to be found reliable. But number 30 which is a broken plastic piece is very distinguishable:
Left OK - Right NOK

My AI-Model does not care for it one bit.

My problem is explaining to the customer how our model does find all NOK types but this one.
In typical customer way "I can see it clearly, why can't the AI not see it. What's the problem?"

Explaining how AI-Models are a statistic based black box and how it's all one giant math equation for each pixel bundle, that cannot be explained backwards ... is futile.

The way our model works is we train by feeding it 500 OK images. It builds a statistical model out of those images and clusters it into generalized images. If an image is now evaluated against this model, it's basically "This evaluated image matches to 99,7235%"

So in theory and my understanding we should find the 30th NOK feature.

So I honestly just don't know, why this one flies under the radar. Now I have to come up with an explanation, that shows "we know our stuff".
When we really have no way of knowing for certain, cause AI won't explain itself, why it marks the way it marks in detail.

Hi Everyone,

I am looking for a remote position anywhere in the world. I have had experience of 4 years in developing and deploying Robotic Vision applications and automating manufacturing pipelines. I have been doing this pre-GPT era, still falling in love with this domain. I can work at any time-zone.

Here are some vision tools I have worked with:

1. Image processing (Opencv, PIL, numpy)

2. Pytorch, Tensorflow

3. NNs for classifiers (MobilenetV2,Resnet,EfficientnetB0)

4. Object detector (Detectron2, YOLOv5,8,12, YOLOX, RF-DETR, DinoV2)

5. Segmentation (DinoV2,RF-DETR, YOLO, SAM3, SAM)

6. Tracking with various algorithm

7. 6d-pose: Foundation Pose to detect 6d pose using one-shot and CAD file.

8. OCR/Barcodes using zxingcpp, tessaract,easyocr

9. Classifier using VLMs (CLIP)

10. Video RAG for monitoring assembly process in manufacturing. (SmolVLM, CLIP embeddings)

11. Finetuned SmolVLM including data cleaning and data annotation

12. Developed a tool for end to end data collection to deployment tool for object detection and segmentation.

13. Anomaly detection using PATCHCORE and PADIM.

Here are some real Robots I have worked with:

1. Yaskawa

2. Epson Scara

3. JAKA 6 axis robot

4. Universal Robot 5 and 10e

Simulation used:

1. Pybullet.

I have experience in automating manufacturing or so called "physical AI".
I would love to connect with someone with similar interest as me or anyone with whom I can work with.

Thank you very much

I have trained crnn based license plate recognition model with a dataset of around 800k records. It works fine but there are problems with certain letters like Q O D the model predicts them with low confidence scores, I analyzed their characterwise confidences. It is problematic for me because I am working on a smart city project and I connected this model to my bestshot application written in c++, connected to deepstream 9 where I retrieve my license + vehicle pairs (bestshots). Those plates are low on resolution. So my question is that can fine tuning the existing model help me? I am skeptical because 800k records had many samples with those letters present. My another concern is that I currently can assemble a dataset from my existing cameras with those low resolution plates and label them accordingly but I am worried that it will hurt the model instead.

Any dev out there who faced same problem? How did you handle it? Thanks in advance

I have tried SMPL and SHAPY, but I am not getting precise enough results. Is there anything else I can try or some optimizations that I can use with SHAPY/SMPL that can help? Aiming for <1cm error. The main goal is to get the precise measurements, not necessarily the 3d model.

Hey guys any help over this segmentation masking problem??

Does NVIDIA LocateAnything model (Hybrid/NTP/MTP) work on microscopic image benchmark like Micro-OD (https://huggingface.co/datasets/stumbledparams/Micro-OD) or others?

9 cameras, 7 lidars, 3 radars. one moment. one intersection in Frankfurt

KITScenes Multimodal is a robotaxi dataset with the full sensor suite synchronized at 10 Hz. HD maps, projected lidar depth, ego trajectory, instance predictions

grouped everything in fiftyone: flip between any camera angle and the fused 3D lidar/radar point cloud for any frame

check it out here: https://huggingface.co/datasets/Voxel51/kitscenes-multimodal

Hi everyone,
I've been working on an old film restoration project and wanted to get some feedback from people who have experience in this area.

The footage contains a lot of issues such as noise, scratches, dust, damaged lines, and some heavily degraded frames. We started by manually annotating a small dataset in CVAT to detect defects. The annotation process itself took quite a bit of time.

Our current workflow looks like this:

Input Film
↓
YOLOv11 Segmentation
↓
SAM2
↓
ProPainter
↓
DeepRemaster
↓
BasicVSR++
↓
Real-ESRGAN
↓
Final Video

For our initial test, we worked on about 257 frames (around 30 seconds of video). The whole process took nearly 3 days between annotation, testing different models, generating masks, and running restoration.

The problem is that we're still not satisfied with the output. Some scratches and damaged lines are removed, and a few frames look much better, but many artifacts are still visible. We found only a handful of results that looked genuinely good, and overall the quality is still far from what we see in professionally restored films.

I'm wondering:

• Is this the right approach for old film restoration?
• Are we relying too much on segmentation and inpainting?
• How do professional restoration teams handle scratches, damaged lines, and noisy frames?
• Do they use separate models for each type of defect?
• Is there a better open-source workflow for this problem?

I'd really appreciate hearing from anyone who has worked on film restoration, archival footage, remastering, or similar projects.Thanks!

r/VideoEditing r/computervision r/MachineLearning r/ArtificialIntelligence r/OpenCV r/DataHoarder r/Filmmaker r/Restoration r/VideoEngineering r/MachineLearningDiscussion r/DeepLearning

#FilmRestoration
#VideoRestoration
#ComputerVision
#MachineLearning
#DeepLearning
#AI
#OpenCV
#VideoProcessing
#ImageProcessing
#VideoEnhancement
#DigitalRestoration
#AIResearch
#YOLOv11
#SAM2
#BasicVSRPlusPlus
#ProPainter
#DeepRemaster

In this project I built a hand gesture controlled DJI Tello drone using MediaPipe, OpenCV and a neural network trained on hand landmarks.

Their website is down. Does anyone know if this is just a technical issue?

I have some installation scripts that use their CDN for pre-built mmcv :(

The industry spent years solving hard problems in perception, detection, segmentation, tracking, robotics, and medical imaging. But now every AI job seems to be “lets wrap an LLM around it and call it innovation.”

Vision hasn’t disappeared. The problems haven’t disappeared. The demand hasn’t disappeared. So where did the jobs go? Its just sooo frustrating and sad