The authors first quantified the representation mismatch by evaluating a frozen Sonata encoder (with a linear probe trained on full scenes) on single-frame camera-coordinate partial views from ScanNet.
Aligning partial point clouds to frozen global 3D encoders without labels
PAGER uses geometric and relational distillation to map camera-coordinate observations into a pretrained semantic space, recovering 65 mIoU from a 2.57 mIoU baseline.
Academic
Akira-Miranda Adeyomi Adeniran-Lowe · Binod Singh · Lars Arnold Dethlefsen · Lazaros Nalpantidis · Theodora Kontogianni
Technical University of Denmark · Pioneer Center for Artificial Intelligence
Research Digest··3 min read
Adeniran-Lowe et al.
Why this paper
From Technical University of Denmark and Pioneer Center for Artificial Intelligence
In one line
PAGER aligns partial 3D point cloud features to a frozen global semantic space using paired geometry, recovering performance lost to coordinate-frame mismatch.
What we could check
- ·No code link found
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ·No stated limitations found
- ✓Reports numbers on named benchmarks (2 benchmarks)
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
§