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Experiments on ShapeNet

Data

We use ShapeNet data and their renderings, as provided by R2N2.

Run

datasets/shapenet/download_shapenet.sh

to download R2N2, and the train/val/test splits. You also need the original ShapeNet Core v1 & binvox dataset, which require registration before downloading.

Preprocessing

python tools/preprocess_shapenet.py \
--shapenet_dir /path/to/ShapeNetCore.v1 \
--shapenet_binvox_dir /path/to/ShapeNetCore.v1.binvox \
--output_dir ./datasets/shapenet/ShapeNetV1processed \
--zip_output

The above command preprocesses the ShapeNet dataset to reduce the data loading time. The preprocessed data will be saved in ./datasets/shapenet and will be zipped. The zipped output is useful when training in clusters.

Training

python tools/train_net_shapenet.py --num-gpus 8 \
--config-file configs/shapenet/voxmesh_R50.yaml

When --copy_data, the preprocessed zipped data from above will be copied to a local /tmp directory. This is particularly useful when training on remote clusters, as it reduces the io time during training

Testing and Evaluation

python tools/train_net_shapenet.py --eval-only --num-gpus 1 \
--config-file configs/shapenet/voxmesh_R50.yaml \
MODEL.CHECKPOINT shapenet://voxmesh_R50.pth

The output of the evaluation produces the results as shown in Table 2 of our paper. To evaluate under the Pixel2Mesh protocol, as in Table 1 of our paper, add --eval-p2m.

Models

Mesh R-CNN Pixel2Mesh SphereInit
voxmesh_R50.pth pixel2mesh_R50.pth sphereinit_R50.pth

Note that we release only the light and pretty for both our and the baseline models.

Performance

Scale-normalized Protocol

Performance of our model on ShapeNet test set under the scale-normalized evaluation protocol (as in Table 2 of our paper).

category #instances chamfer normal F1(0.1) F1(0.3) F1(0.5)
bench 8712 0.120899 0.657536 42.4005 86.0036 95.128
chair 32520 0.183693 0.712362 31.6906 79.8275 92.0139
lamp 11122 0.413965 0.672992 30.5048 70.3449 84.5068
speaker 7752 0.253796 0.730829 24.8335 74.6606 88.237
firearm 11386 0.168323 0.621439 47.2251 85.271 93.8171
table 40796 0.148357 0.75642 42.249 86.2039 94.1623
watercraft 9298 0.224168 0.642812 30.0589 75.5332 89.9764
plane 19416 0.187465 0.684285 39.009 80.998 92.1069
cabinet 7541 0.111294 0.75122 34.8227 86.9346 95.371
car 35981 0.107605 0.647857 29.6397 85.7925 96.2938
monitor 5256 0.218032 0.779365 27.2531 77.2979 90.904
couch 15226 0.144279 0.72302 27.5734 81.684 94.3294
cellphone 5045 0.121504 0.850437 42.9168 88.9888 96.1367
total 210051 0.184875 0.710044 34.629 81.5031 92.5372
per-instance 210051 0.171189 0.70275 34.9372 82.4107 93.1323

Pixel2Mesh Protocol

Performance of our model on ShapeNet test set under the pixel2mesh evaluation protocol (as in Table 1 of our paper). To evaluate under this protocol, add the --eval-p2m flag.

category #instances chamfer normal F1(0.0001) F1(0.0002)
bench 8712 0.000295252 0.657508 73.4681 84.4999
chair 32520 0.000400415 0.712348 66.5227 79.3634
lamp 11122 0.000788915 0.673057 60.1057 71.7711
speaker 7752 0.000582152 0.730797 59.9974 73.8792
firearm 11386 0.000357016 0.621438 75.9761 85.5111
table 40796 0.000342991 0.756442 76.0776 85.4878
watercraft 9298 0.000449061 0.642791 62.808 76.5464
plane 19416 0.000313141 0.684333 75.8104 85.3897
cabinet 7541 0.000293613 0.751306 72.6302 84.7327
car 35981 0.000240585 0.647896 71.8118 85.5155
monitor 5256 0.000470965 0.779397 64.2917 77.8422
couch 15226 0.000355369 0.723013 64.1388 79.327
cellphone 5045 0.000280397 0.850456 77.3011 87.8698
total 210051 0.000397682 0.71006 69.303 81.3643
per-instance 210051 0.000368317 0.702768 70.4479 82.3373