Struggling to make sense of depth maps and camera trajectories from your SLAM or depth estimation pipeline? You’re not alone. Common pain points include noisy depth values, unclear confidence thresholds, large point‑cloud files that crash viewers, and drift in estimated camera paths over long sequences. Here’s a quick, practical checklist to tighten up the workflow.
First, visualize raw RGB, depth, and confidence side‑by‑side using a simple matplotlib grid. Apply percentile clipping (e.g., 2‑98%) to depth images so extremes don’t swamp the colormap, and overlay a binary confidence mask with a clear threshold (THR). Plot the confidence histogram on a log scale and overlay the chosen threshold; this instantly shows how many pixels you keep per frame.
Next, inspect the camera trajectory in both 3D and top‑down views. Plot the trajectory line, mark start and end, and overlay a random subset of world points for context. If the path looks jittery, reduce the keyframe interval or switch to windowed mode to limit KV‑cache growth.
When exporting, save points as binary PLY for fast loading in MeshLab/CloudCompare, compress poses and depth as half‑float NPZ, and optionally generate a GLB for web sharing. For interactive exploration, launch a lightweight Viser viewer on port 8080—just a few pip install lines and a threaded start.
If you hit memory limits, run an ablation over keyframe_interval values to trade FPS for trajectory length, and pick the setting where drift stays acceptable. For outdoor scenes, add a sky‑masking step to remove spurious far‑field points.
These steps turn raw outputs into clean, actionable visualizations and shareable models without reinventing the wheel.
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