Pre-trained weights

Usage:

import torch

ddpm, lidar_utils, cfg = torch.hub.load(
    repo_or_dir = "kazuto1011/r2dm",
    model       = "pretrained_r2dm",
    config      = "r2dm-h-kitti360-300k", # default
    device      = "cuda",
)

KITTI-360 (64x1024, spherical projection)

Config	Loss	Depth	Positional Encoding	FRD (T=256) ↓	torch.load kwargs
A	L2	Log	Identity	202.40	config="r2dm-a-kitti360-300k"
B	L1	Log	Identity	382.35	config="r2dm-b-kitti360-300k"
C	Huber	Log	Identity	174.83	config="r2dm-c-kitti360-300k"
D	L2	Metric	Identity	229.28	config="r2dm-d-kitti360-300k"
E	L2	Inverse	Identity	188.84	config="r2dm-e-kitti360-300k"
F	L2	Log	w/o spatial bias	910.67	config="r2dm-f-kitti360-300k"
G	L2	Log	Spherical harmonics	180.60	config="r2dm-g-kitti360-300k"
H	L2	Log	Fourier features	153.73	config="r2dm-h-kitti360-300k" (default)

KITTI-Raw (64x512, scan unfolding)

Config	Loss	Depth	Positional Encoding	FRD (T=1024) ↓	torch.load kwargs
H	L2	Log	Fourier features	207.31	config="r2dm-h-kittiraw-300k"