ECCV'26 开源 | UniPR-3D：首个基于3D基座模型的视觉识别框架，不用标定、不看纹理，单帧序列全面碾压SOTA！

TL;DR - UniPR-3D (SJTU, NTU, Zaragoza, Univ. of Macau; ECCV'26, open-sourced) is the first visual place recognition framework built on a 3D foundation model (VGGT), fusing 2D texture tokens with emergent 3D geometry tokens into one global descriptor. It matters because it makes place recognition robust to seasonal, lighting, and viewpoint changes that break texture-only VPR — a core capability for SLAM, robot navigation, and autonomous driving.

• Pipeline: DINOv2 produces 2D cls/register/patch tokens; patch tokens then pass through VGGT's alternating frame + global attention to yield 3D camera/register/patch tokens. The 3D camera token is discarded for viewpoint robustness, and no camera intrinsics/extrinsics are required — geometry emerges from raw RGB.
• Token-specific aggregation: GeM pooling + light MLP for the few cls/register tokens; Optimal Transport with Sinkhorn soft matching (plus a "dustbin" bin to discard uninformative regions) for the many patch tokens. Five descriptors (2D cls/register/patch, 3D register/patch) are concatenated.
• Variable-length sequence matching uses an anchor frame plus support frames — cross-frame GeM+MLP on register tokens, clustering then OT on patch tokens — so inference accepts arbitrary sequence lengths without retraining.
• Results: reported SOTA on most of 10 single-frame benchmarks (vs. NetVLAD, SALAD, MegaLoc) and 4 sequence benchmarks (vs. SeqSLAM, SeqNet, CaseVPR), with >10 point R@1 gain on Oxford at the strict 2m threshold and large gains on the four-season Nordland set. Ablations show 2D and 3D patch tokens are complementary, while explicitly injecting 3D pose hurts (geometry is already implicitly encoded). Cost is a modest inference-latency increase.