Updated 54 min ago · first seen 11 Sept 2026
paper_01M294FRKD3Y93CZRGJBXTCGEQ
- Published
- 11 Sept 2026
- T1 · 55 min ago
- arXiv
- 2609.11894
- T1 · 55 min ago
- Category
- cs.CV
- T1 · 55 min ago
Abstract
Solving novel view synthesis (NVS) for millimeter-wave (mmWave) radar requires a renderer that is physically faithful, complex-valued, and multi-viewpoint-tractable. No prior method achieves these three properties simultaneously. Differentiable Monte Carlo (MC) ray tracers implement the radar forward model directly with explicit material modeling and complex outputs, but do not scale to the multi-view optimization NVS demands. Optical-NVS ports of NeRF, hash grids, and 3D Gaussians train fast but discard phase and replace explicit material modeling with opaque learned features, restricting them to power-only range-azimuth (RA) magnitudes. We propose 3D Point Splatting (3DPS), the first differentiable point renderer for radar, derived directly from the standard solid-angle form of the radar equation. Each oriented 3D point carries an ITU-R P.2040 material model, evaluated in closed form, with the resulting complex phasor splatted into range bins through a precomputed point spread function (PSF). The complex-valued output makes the renderer product-agnostic. The same optimized scene yields analog-to-digital converter (ADC), complex range profile (CRP), and RA outputs through standard fast Fourier transform (FFT) pipelines without retraining for each format. On six outdoor ColoRadar scenes, 3DPS reaches 0.587 mean Pearson correlation on held-out RA images. This is between 1.7x and 5.2x the three optical-NVS baselines (RadarSplat, Radar Fields, DART). Training takes approximately 3 minutes per scene on a single RTX 4090.
Authors 3
Adnan Armouti, Yixuan Gao, Rajalakshmi Nandakumar
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 54 min agohigh
- arXiv id
- 2609.11894
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
- Categories
- cs.CV, cs.GR, cs.LG, eess.SP
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
- Primary category
- cs.CV
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Each value shows its source, tier and observation time. Conflicting claims are kept side by side and flagged — never averaged. How AI Atlas records facts →
Provenance
Attributed facts
9
Source tiers
T19
Freshest observation
54 min ago
Conflicts
None
No models linked to this paper yet.
As of
Rewind the record: see this entity's attributes exactly as AI Atlas knew them on a given day.
Claim history · Authors
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
3D Point Splatting for mmWave Radar Novel View Synthesis: arxiv announce type changed from cross to new
Arxiv announce typecross→newarxivNew paper: 3D Point Splatting for mmWave Radar Novel View Synthesis
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.CV | feed | T1· Official | 54 min ago | 1 |
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.LG | feed | T1· Official | 55 min ago | 1 |
Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.