your 3D reconstruction pipeline treats its camera poses as ground truth. PIVOT ships two poses for every frame, and they disagree

5 real scenes, 103 drone trajectories, a shared sparse point cloud per scene, per-frame pose error precomputed. all of it captured by one independent researcher with a single DJI Mini 4 Pro on the grounds of an irish castle

i encoded every trajectory as an MCAP episode for fiftyone: scrub a flight and watch both camera frustums move through the same point cloud, with a line drawn between them showing exactly how far apart "where the drone said it was" and "where COLMAP thinks it was" are at every frame

load it in one line: https://huggingface.co/datasets/Voxel51/PIVOT-mcap

get hands-on without writing any code, it's running in a HF space: https://huggingface.co/spaces/harpreetsahota/PIVOT-mcap-explorer

Has anyone been getting this issue with roboflow

Hi everyone,

I am looking to buy 1–2 units of an industrial camera for a project in Bengaluru, India.

My preferred camera is e-con Systems RouteCAM_CU20 with the Sony STARVIS IMX462 sensor. I need the rugged/outdoor version, ideally with an IP67-rated enclosure.

Required specifications:
- Sony IMX462 or a similar ultra-low-light / NIR-sensitive sensor
- 1920 × 1080 / around 2 MP
- GigE Vision / Ethernet interface
- PoE preferred
- IP67-rated enclosure for outdoor or dusty/wet use
- M12 lens mount or suitable fixed-lens option
- Availability and shipping to India
- Quantity: initially 1–2 units for evaluation

I found the RouteCAM_CU20, but the e-con Systems store does not ship this configuration to India. I understand that the IP67 CU20 may be a custom configuration rather than a normal web-store SKU.

Does anyone know:
1. An Indian e-con Systems distributor or reseller who can supply it?
2. A reliable supplier that ships this or an equivalent camera to India?
3. An alternative IP67 PoE GigE camera with similar IMX462 low-light/NIR performance?

I need an actual purchasable source, not only a datasheet or manufacturer contacts

Thanks.

This is a continuation of last week’s post where I had the models compete in a Three.js cup and ball game. This time, I’m using real-world footage, which is even more challenging because of distractors. I might test this out on some of the Anthropic models sometime. 

I've been experimenting with compact visual representations for edge/perception systems, where the goal is to preserve useful machine-level information while significantly reducing the representation size.

One thing I've noticed is that the degradation isn't uniform.

Global scene understanding can remain surprisingly stable while localization and small-object information start degrading much earlier.

Increasing input resolution alone also doesn't necessarily recover that information. In one of my experiments, a higher-resolution branch improved access to spatial detail but still couldn't reproduce the semantic quality of the deeper teacher representation.

It made me think there are really two different things being lost during aggressive representation compression:

1. Spatial information — where something is and fine local details.

2. Semantic interaction/context — relationships between patches/objects that deeper transformer layers learn.

Simply increasing spatial resolution seems to address mainly the first problem.

I'm currently thinking about this as a rate–semantics tradeoff, rather than a traditional image-compression problem:

Image → semantic encoder → compact representation → downstream tasks

where the objective isn't pixel reconstruction but preserving enough information for detection, classification, depth, etc.

For people working with ViTs, representation learning, edge perception, or learned compression:

What have you found is usually the first thing to collapse as representation size decreases — spatial detail, feature diversity, global context, or something else?

And have you found good ways of measuring this beyond downstream mAP/accuracy?

Built a javelin throw analysis pipeline using YOLO11 OBB and pose estimationI developed a computer-vision system that combines a custom-trained YOLO11s-OBB model, YOLO11 Pose, OpenCV, ByteTrack, and BoT-SORT to analyze javelin throws from broadcast footage.

It detects and tracks the javelin, identifies the athlete’s throwing arm, calculates a smoothed elbow angle, recognizes throw phases, and draws the post-release trajectory.

The main challenges included motion blur, false detections, tracking-ID changes, camera movement, missed detections, and selecting the correct athlete.

GitHub:https://github.com/chahmadraza89/computer_vision

I’m trying to calibrate my iPhone camera using a ChAruCo board. Even though I’m familiar with the concepts, this is my first time actually implementing them. I’ve already built a pipeline and ran the calibration using nearly 40 photos of the ChAruCo board taken from various angles. However, I can’t get my reprojection error below 1.8px, which is way above the recommended 0.3–0.5px range. I'm not sure if the issue lies with the pipeline itself or the photos I took.

My end goal is to build a monocular pipeline to measure the L x W x H of cardboard boxes for a delivery company. The measurements are taken in a controlled environment: boxes are always placed on a fixed surface with ChAruCo markers laid out for reference, and the pictures taken of the boxes will always show three sides. I have already built the box segmentation (using a SAM base model) and the box skeleton-building pipeline (using OpenCV) which is working fine. Right now, I'm achieving an accuracy ranging from ~2 cm to ~15 cm (some are hits, and some are misses). I need to improve this consistency, and I suspect camera calibration is the biggest contributing factor. My target accuracy is ~2 to ~5 cm (that’s the absolute ceiling, but the lower, the better).

And also other than the calibration issue, I need some advice on how to arrange the box placement surface. How should the ChAruCo marker be oriented? Should I place multiple ChAruCo markers on it? For context, this surface is the physical scale where they weigh the boxes. The basic requirement is that a photo of the box is taken from a handheld device (like a mobile phone), and the system calculates the dimensions. I'm currently trying this on my iphone 17 but the actual device will be much worse than it. But first I need a working pipeline.

Refer to the calibration code here - https://drive.google.com/file/d/1UI95GfYpjCB84QIEAWv5XFApzdLXuAZE/view?usp=sharing

World Labs just revealed their new omni world model, Atlas, and the space-time simulation features look like a massive leap for novel view synthesis and robotics training.

A few standout technical claims from their space-time simulation drop:

• Sparse-View "Bullet Time": They are generating frozen-time, free-camera multiview fly-throughs using footage from as few as three to five ordinary cell phones. No calibrated camera arrays, green screens, or professional capture studios are required to reframe the shots.
• Generative Real-to-Sim: For robotics, it doesn't just reconstruct a static 3D environment. As a simulated robot moves through the space, Atlas actively generates the RGB and depth data the robot's body-mounted cameras would observe along that specific trajectory.
• Physical Interactions: From casual real-world recordings, the model aids in building simulations that capture how objects move and interact. It supports rigid, articulated, and deformable objects, allowing users to dynamically alter lighting, backgrounds, and object positions to generate diverse robotic training data.
• Under the Hood: It’s built as a multimodal autoregressive diffusion transformer. Unlike models that rely on coarse text prompting for camera movement, Atlas uses precise camera geometry as a native input to ground everything in a shared "spatial context."

I’m working on a little project to benchmark how vision-language models do with classifying grappling techniques. These results are the vanilla models without any fine-tuning, so it’s sort of hit or miss. I’m sure with enough data, the guesses can get pretty accurate. If any of you fellow grapplers who are engineers are interested in playing around with this, I’d be happy to open source it. 

Single-script OCR works fine for me, but shopsigns and menus with chinese + english (or hindi + english) fall apart. detection boxes get messy and it starts predicting characters from the wrong script.

Do you run script identification first and route to separate recognizers, or train one model on mixed data and let it sort it out? and roughly how much mixed-script data before it stops breaking?

Also, does angled or curved text make this worse for you? most public benchmarks i've seen are clean horizontal text.

Hi,

I've been trying to train custom model (3 classes) for yolox small (416px) on my dataset and no matter what I try - I get too many false positives. Is there something very basic I'm missing? What could be the route to figure out why is so?

Trained with mixup, without mixup, with augmentations, little augmentations, added more backgrounds (even those that cause false positives), trained for 100 epochs, 300 epochs, exact official config used.

Dataset is of mostly coco images (person, truck/car-vehicle) and drones. Checked added images/bboxes/etc.

here's my dataset data
train (instances_train2017.json)

Images: 43803

Background images: 1985

Images per class:

drone: 7758

person: 24103

vehicle: 19772

val (instances_val2017.json)

Images: 4831

Background images: 192

Images per class:

drone: 1066

person: 2159

vehicle: 2431

Still can't figure out why it tends to give so much false positives (with quite high confidece of 80+).

Hi Everyone,

I’d like to introduce SAMannot, an open-source tool we developed for efficient, interactive video instance segmentation and annotation using Meta’s Segment Anything Model 2 (SAM2).

SAMannot was designed for research workflows where accurate pixel-level video annotations are needed, but fully manual annotation is too time-consuming. It runs locally, so videos do not need to be uploaded to a cloud service, and it uses a memory-efficient workflow to make SAM2 practical for videos and limited GPU resources.

Some of the main features are:

• SAM2-assisted interactive video segmentation with auto-propagation across frames
• persistent object/instance identities
• correction and refinement of propagated masks
• block-based, efficient processing to keep CPU and GPU memory requirements manageable
• automatic-prompt generation across blocks
• export of segmentation masks in PNG and polygon formats (YOLO label format)
• fully local and open-source

We originally developed and evaluated SAMannot in research workflows including animal behaviour videos, but the tool is intended to be general-purpose and may also be useful for other scientific video annotation tasks. We also evaluated it on subsets of the DAVIS and LVOS benchmarks.

Project page: https://samannot.github.io/
Paper: SAMannot: A Memory-Efficient, Local, Open-Source Framework for Interactive Video Instance Segmentation Based on SAM2 | Journal of Open Research Software

The code is released under the MIT license.

We would be very interested in any feedback.

Thanks,
Anna
on behalf of the SAMannot authors