r/computervision

I trained my own model on 750,000 images for 45 hours.

Then I walked into my university hall with nothing but my phone and recorded an 11-minute video.

No LiDAR.

No depth sensor.

No specialized camera setup.

Just monocular vision, a single RGB camera.

And in some of my tests, my model even outperformed YOLO26x.

From a single phone video to depth estimation and 3D reconstruction.

750K training images. 45 hours of training. 11 minutes of footage. One camera. No LiDAR.

I’ve been experimenting with Apple’s ML-SHARP model and built a small open-source project around it called Spatial Photos.

ML-SHARP estimates a 3D Gaussian splat from a single RGB image. I then take that reconstruction and convert it into a much smaller and simpler representation representation. It uses layered textured meshes stored in a custom file format I called .spatial. It still allows the the same sort of limited viewport shifting intended by ML-SHARP without compromising quality.

That lets the result stay relatively small and render interactively in a normal web browser, as you can see in the GIF.

Demo: https://www.spatialphotos.dev/

Source: https://github.com/frozein/SpatialPhotos

Here's what my format looks like from a faraway angle

Been working on a volleyball analysis pipeline, this is the current output on a full rally sequence.

The input is just the hall's automatic broadcast camera, so it pans and zooms. No fixed camera, no calibration.

Rough pipeline:

- Detection: RF-DETR Medium at 1280, fine-tuned on of my own labeled frames (ball + person). The ball is tiny (median ~22 px), which is why I stayed at 1280 instead of 640. Test AP50 is 0.92 for the ball, 0.97 for people.

- Camera motion: phase correlation between frames.

- Court: per-frame homography fitted to the white court lines, checked against the floor colour, so everything can be expressed in court metres.

- Ball: my own Kalman tracker with a gravity term, to bridge missed detections and catch bounces and hits.

- Pose: RTMPose on every frame, used for which hand/body part touched the ball and for the first referee's arm signals.

- Players: 6+6 tracks per team, plus referees and bench told apart by position (and shirt colour for the officials).

- Scoreboard: macOS Vision OCR on the broadcast graphic.

- On top of that, a rule layer (FIVB rules) that turns all of this into touches, blocks, rallies and points, and flags it when the scoreboard, the referee and the ball disagree.

It also estimates ball speed per hit (2D, so a lower bound), attack zone and direction, serve position and whistle-to-serve time, landing point in/out, and running distance (experimental).

Hardest parts so far:

- the ball leaving the top of the frame and the tracker jumping to something else

- referee signals: in this hall two staff stand behind the first referee and their boxes are taller than his (his legs are hidden by the stand), so picking "the tallest person on the stand" read the wrong arms

- every new hall = new floor colours, lines, lighting and camera angle

Yes it is all vibe coding with Claude

https://youtu.be/xbSyAW-H-b8

I spent a couple of months vibe-coding an automated boxing analytics pipeline.
SAM 3 for fighter tracking, with the canvas and ropes as pixel masks. OSNet re-ID keeps identities through occlusions. RTMPose and a small classifier detect punches by hand and type. The ring masks fit a ring model for top-down positions, with camera-motion compensation so handheld shake doesn't pollute the numbers. SAM 3D Body adds metric distance between fighters.

Some processed fights here.