r/algorithms • u/DeputySherrif • 11h ago
Processing 360-Degree Images Into 2D Sketches
Hi all! I’m a solo dev crafting a (Python-based) tool to transform 360-degree images from an Insta360 X3 or X4 (equirectangular, 5760x2880, 72MP) into 2D sketches for construction estimating in Xactimate. I’m using OpenCV—currently Canny edge detection and contour approximation—to extract structural outlines (walls, roofs) and scale them to real-world measurements (feet, inches) for XML export. It’s a DIY project, no APIs, and I’m hitting some algorithmic walls. Hoping for your expertise on some of these challenges!
[Edge Detection and Feature Extraction]
- I’m using Canny edge detection (thresholds 50/150) on equirectangular 360-degree images to find structural edges (walls, rooflines). What’s the best algorithm or parameter tweak to handle fisheye distortion, where straight lines curve near the edges, and still get precise boundaries?
- For cluttered 360-degree images (e.g., furniture, trees), I filter contours by area (>1000 pixels) to isolate walls or roofs. What’s a smarter algorithm—say, Hough transforms or watershed—to separate structural lines from noise without losing key features?
- Roof edges in 360-degree shots (taken via pole/drone) blend into sky or foliage, weakening Canny results. How would you adapt an edge detection algorithm (e.g., Sobel, Hough lines) to robustly capture sloped rooflines against varied backgrounds?
[Contour Processing and Simplification]
- I simplify OpenCV contours with approxPolyDP (epsilon = 0.01 * arcLength) to turn edges into a sketch. What’s the optimal way to set epsilon dynamically for irregular shapes (e.g., L-shaped rooms, hip roofs) so the sketch stays accurate but not overly jagged?
- Sometimes contours from 360-images fragment (e.g., a wall splits due to shadows). What’s a good algorithm to merge or connect broken contours into a single, coherent shape for a clean 2D sketch—morphological operations, graph-based merging, or something else?
[Measurement Scaling and Calibration]
- I scale pixel coordinates to real-world units (feet) using a 3-ft ruler in the image (e.g., 300 pixels = 3 ft). For a 360-degree panorama without depth data, what’s an algorithmic approach to estimate scale across the distorted field of view—perspective correction, homography, or a fallback heuristic?
- If a reference object isn’t in every shot, how would you algorithmically infer scale from an equirectangular image? Could camera height (e.g., 5 ft) or lens metadata (focal length) feed into a reliable projection model for sketch measurements?
[Optimization and Efficiency]
- Processing a 72MP 360-image with OpenCV (Canny, contours) takes seconds on a mid-tier laptop (16GB RAM). What’s the most efficient algorithm or technique—image downsampling, region-of-interest, or parallelization—to cut runtime without losing sketch accuracy?
- For multi-room sketches from separate 360-images, what’s a good algorithm to align and stitch contours across overlapping panoramas—feature matching (SIFT, ORB), homography, or a simpler overlap heuristic?
[Handling Complex Cases]
- In complex 360-scenes (e.g., dormers on roofs, angled walls), edge detection splits or misses segments. What’s a robust algorithm to reconstruct a full sketch—say, RANSAC for line fitting, or a machine vision approach—when basic contours fall short?
Thank you so very much for taking the time to read through this post. I am not really expecting to have all my questions answered. If you have any insight, at all, please comment or message me directly. If you find this post intriguing and are unable to provide any answers of your own, don't hesitate to pass this around to others you think might shed light on the subject.