Visualization

💡PCA features visualization for a pair of LiDAR and image in the validation set.

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Top row: PCA features of DINO
Bottom row: PCA features of projected voxel features

Top row: PCA features of DINO Bottom row: PCA features of projected voxel features

Experiments

Oxford RobotCar

VXP v2 outperforms VXP in cross-modal place recognition

Recall@1 2D-2D 3D-3D 2D-3D 3D-2D
Cattaneo 88.40 93,99 41,92 29,51
LC2 - - - - - - - -
liploc 59.5744 70.4453 32.6137 26.7000
vxp 92.0218 94.6916 44.6033 30.8090
vxpv2 88.5861 96.2959 63.4741 58.2897
Recall@1% 2D-2D 3D-3D 2D-3D 3D-2D
Cattaneo 96.6 98.4 77.3 70.4
LC2 84.1 83.0 81.2 73.8
liploc 90.2036 92.2567 77.7881 73.6100
vxp 98.7965 98.8433 84.3917 76.9310
vxpv2 98.2309 99.4591 95.1170 93.9706

ViViD++

VXP outperforms VXP v2 in both uni and cross-modal place recognition

Recall@1 2D-2D 3D-3D 2D-3D 3D-2D
LC2 69.2 58.1 60.9 51.8
cattaneo 93.38925 90.97315 87.58360 78.5841
liploc 61.10215 78.76445 73.69395 54.9198
vxp 96.67990 96.98190 96.78070 94.5331
vxpv2 93.35920 89.20055 84.97145 84.5016
Recall@1% 2D-2D 3D-3D 2D-3D 3D-2D
LC2 96.9 96.1 96.0 94.6
cattaneo 99.8322 99.89935 99.63085 98.62375
liploc 93.9596 97.38295 98.42260 93.01880
vxp 99.8994 99.73170 99.63110 99.79880
vxpv2 99.3293 98.62510 98.18890 98.45735