scripts

Train and Eval Documentation for 3DGEER

Train Examples

Will be soon released.

Render and Eval Examples

ScanNet++ Dataset

bash scripts/render_scnt.sh 1d003b07bd/dslr data/scnt/datasets ckpt/scnt KB
bash scripts/eval_scnt.sh 1d003b07bd/dslr data/scnt/datasets ckpt/scnt KB
bash scripts/eval_scnt.sh 1d003b07bd/dslr data/scnt/datasets ckpt/scnt BEAP

Aria Dataset

bash scripts/render_scnt.sh steakhouse data/aria/scannetpp_formatted ckpt/aria KB
bash scripts/eval_scnt.sh steakhouse data/aria/scannetpp_formatted ckpt/aria KB
bash scripts/eval_scnt.sh steakhouse data/aria/scannetpp_formatted ckpt/aria BEAP

Tanks and Temples

bash scripts/render_scnt.sh truck data/tt/datasets ckpt/tt PH
bash scripts/eval_scnt.sh truck data/tt/datasets ckpt/tt PH
bash scripts/eval_scnt.sh truck data/tt/datasets ckpt/tt BEAP

Asso Mode Ablation

The --asso_mode argument controls the Gaussian association (tile culling) method used during rendering. Three modes are supported:

asso_mode	Method	Description
0	PBF (default)	Particle Bounding Frustum — exact and tight association
1	EWA	AABB via Elliptical Weighted Average
2	UT	AABB via Unscented Transform

To run the ablation, pass --asso_mode <value> directly to render.py. Example using ScanNet++ with KB mode:

# PBF (default, asso_mode=0)
python render.py -m ckpt/scnt/1d003b07bd/dslr -s data/scnt/datasets/1d003b07bd/dslr \
    --iteration 30000 --camera_model FISHEYE --render_model KB --skip_train \
    --sample_step 0.0015 --fov_mod 2.0 --train_test_exp \
    --raymap_path data/scnt/datasets/1d003b07bd/dslr/raymap_fisheye.npy \
    --asso_mode 0

# EWA (asso_mode=1)
python render.py -m ckpt/scnt/1d003b07bd/dslr -s data/scnt/datasets/1d003b07bd/dslr \
    --iteration 30000 --camera_model FISHEYE --render_model KB --skip_train \
    --sample_step 0.0015 --fov_mod 2.0 --train_test_exp \
    --raymap_path data/scnt/datasets/1d003b07bd/dslr/raymap_fisheye.npy \
    --asso_mode 1

# UT (asso_mode=2)
python render.py -m ckpt/scnt/1d003b07bd/dslr -s data/scnt/datasets/1d003b07bd/dslr \
    --iteration 30000 --camera_model FISHEYE --render_model KB --skip_train \
    --sample_step 0.0015 --fov_mod 2.0 --train_test_exp \
    --raymap_path data/scnt/datasets/1d003b07bd/dslr/raymap_fisheye.npy \
    --asso_mode 2

Evaluation Protocol

Unlike Fisheye-GS and 3DGUT, which remap the ScanNet++ ground-truth images into an equidistant projection space for evaluation, we perform evaluation directly under the original camera model (KB) of the dataset.

For methods using different projection models (e.g., equidistant, pinhole, or BEAP), the rendered outputs must be remapped back to the original camera space before comparison with the ground truth.

However, such remapping inevitably introduces pixel-level interpolation shifts, which can distort perceptual metrics (e.g., PSNR / SSIM / LPIPS) and prevent them from faithfully reflecting the actual scene reconstruction quality.

To ensure pixel-wise alignment, we apply the same remapping operation to the ground-truth images as well. This guarantees that both predictions and ground truth undergo identical interpolation, eliminating evaluation bias caused by projection conversion.

For the ray mapping used in this process (to reproduce the numbers in Table K.2), we use the grid map provided by:

https://huggingface.co/yuliangguo/depth-any-camera/blob/main/scannetpp_dac_swinl_indoor_2025_01.zip

Or run:

  python data/scnt/scnt_raymap_dac.py 

Qualitative comparisons can be found in Figure K.4.

Figure K.4.; https://arxiv.org/abs/2505.24053