Point density, not architecture, was the bottleneck for a 5-class radar-only object [P]

Hello all, TL;DR: point density, not model architecture, was the real bottleneck for a 5-class radar-only classifier on RadarScenes. Going from 1 to 5 points per instance roughly doubles macro F1 (0.381 → 0.764), while a whole set of architecture and feature changes all landed inside a measured noise floor. Real failure case attached: a stationary two-wheeler misread as a pedestrian. Setup I'm a perception / radar signal processing engineer getting into ML on radar data. Trained a 5-class classifier (car, large_vehicle, two_wheeler, pedestrian, pedestrian_group) on RadarScenes radar point clouds only, no camera or lidar. Per-instance histogram (16 bins) encoding into a 3-layer MLP. Main result: point density is the ceiling Macro F1 goes from 0.381 to 0.764 just by increasing points per instance from 1 to 5. Same trained model, nothing else changed. Why: a single point can't carry a size or velocity-spread signature. large_vehicle's F1 is 0.037 at n=1 vs 0.995 at n=11+. Some classes still work at n=1 (car separates cleanly on RCS/Doppler alone), others don't (two_wheeler and pedestrian collapse to the same near-zero-velocity signature when sparse). Ablation studies Wider/deeper networks, six alternative feature encodings, different bin edges, all landed inside the noise floor I measured with a 6-fold split sensitivity check (same train/val/test proportions, sequences reassigned per fold). Closest thing to an exception: swapping the histogram for explicit per-instance statistics (mean/median/std) actually made things slightly worse (0.658 vs baseline's 0.686), and pedestrian's own F1 fell outside its class-specific noise floor. Data caveats RadarScenes is naturalistically collected, not balanced: common classes get broad coverage, rare ones don't. two_wheeler merges two physically different speed regimes (bicycle vs a much rarer motorized variant). large_vehicle merges large_vehicle/truck/train/bus, RadarScenes' own recommended scheme, mostly forced by data scarcity i