about the dataest

[zzhmx]

May I ask how many lines of point cloud data are scanned in your dataset? After converting my 128 line radar into range images, it is still very sparse. If there are too many clicks, an error will be reported: Runtimeerror the expanded size of the tensor (150000) must match the existing size (159400) at non-singleton dimension 0. Target size (150000,3). Tensor size:(159400,3)

[jbehley]

I think the 150k points is just an upper bound that we used for the KITTI dataset (with 64 beams). Therefore, you might have to simply increase this in the configuration, e.g., https://github.com/PRBonn/lidar-bonnetal/blob/master/train/tasks/semantic/config/arch/darknet53.yaml

lidar-bonnetal/train/tasks/semantic/config/arch/darknet53.yaml

Lines 77 to 100 in 5a5f4b1

	dataset:
	labels: "kitti"
	scans: "kitti"
	max_points: 150000 # max of any scan in dataset
	sensor:
	name: "HDL64"
	type: "spherical" # projective
	fov_up: 3
	fov_down: -25
	img_prop:
	width: 2048
	height: 64
	img_means: #range,x,y,z,signal
	- 12.12
	- 10.88
	- 0.23
	- -1.04
	- 0.21
	img_stds: #range,x,y,z,signal
	- 12.32
	- 11.47
	- 6.91
	- 0.86
	- 0.16

[zzhmx]

I think the 150k points is just an upper bound that we used for the KITTI dataset (with 64 beams). Therefore, you might have to simply increase this in the configuration, e.g., https://github.com/PRBonn/lidar-bonnetal/blob/master/train/tasks/semantic/config/arch/darknet53.yaml我认为 150k 个点只是我们用于 KITTI 数据集（有 64 个光束）的上限。因此，您可能只需在配置中增加此设置，例如 https://github.com/PRBonn/lidar-bonnetal/blob/master/train/tasks/semantic/config/arch/darknet53.yaml

lidar-bonnetal/train/tasks/semantic/config/arch/darknet53.yaml

Lines 77 to 100 in 5a5f4b1

	dataset:
	labels: "kitti"
	scans: "kitti"
	max_points: 150000 # max of any scan in dataset
	sensor:
	name: "HDL64"
	type: "spherical" # projective
	fov_up: 3
	fov_down: -25
	img_prop:
	width: 2048
	height: 64
	img_means: #range,x,y,z,signal
	- 12.12
	- 10.88
	- 0.23
	- -1.04
	- 0.21
	img_stds: #range,x,y,z,signal
	- 12.32
	- 11.47
	- 6.91
	- 0.86
	- 0.16

thank you!