Could u release the single Lidar model Occ weight？

[SodaKy0401]

And I‘m curious about is there any other single Lidar Occ precetion model?

[SodaKy0401]

If I just wanna send only the lidar data to test,what should i do ?

[AlphaPlusTT]

We also run a LiDAR-only variant in our application, mainly because image-based BEV features are quite sensitive to the camera setup. The LiDAR-only code is not ready for release yet (it contains some unpublished components), but I plan to share it in the future.
If you’d like to try it yourself before then, here’s a possible approach:

1. Use the provided config as a reference to train a LiDAR-only model in PyTorch.
2. Convert it with the TensorRT script in CUDA-DAOcc.
3. Adapt the TensorRT inference code to skip the image input branch.

voxel_size: [0.075, 0.075, 0.2]
point_cloud_range: [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]

augment3d:
  scale: [1., 1.]
  rotate: [-0., 0.]
  translate: 0.

model:
  type: BEVFusion
  encoders:
    lidar:
      voxelize:
        max_num_points: 10
        point_cloud_range: ${point_cloud_range}
        voxel_size: ${voxel_size}
        max_voxels: [ 120000, 160000 ]
      backbone:
        type: SparseEncoder
        in_channels: 5
        sparse_shape: [ 1440, 1440, 41 ]
        output_channels: 128
        order:
          - conv
          - norm
          - act
        encoder_channels:
          - [ 16, 16, 32 ]
          - [ 32, 32, 64 ]
          - [ 64, 64, 128 ]
          - [ 128, 128 ]
        encoder_paddings:
          - [ 0, 0, 1 ]
          - [ 0, 0, 1 ]
          - [ 0, 0, [ 1, 1, 0 ] ]
          - [ 0, 0 ]
        block_type: basicblock
    camera: null
  fuser: null
  decoder:
    backbone:
      type: CustomResNet
      stride: [1, 1, 2]
      numC_input : 256  # 256 in lidar branch
      num_channels: [128, 256, 512]
    neck:
      type: FPN_LSS
      in_channels: 640
      out_channels: 512  # TODO: [yz] test 128 as in occ  # 512 in lidar branch
      scale_factor: 2
      extra_upsample: null
  heads:
    map: null
    object:
      type: CenterHead
      in_channels: 512
      train_cfg:
        point_cloud_range: ${point_cloud_range}
        grid_size: [ 1440, 1440, 41 ]
        voxel_size: ${voxel_size}
        out_size_factor: 8
        dense_reg: 1
        gaussian_overlap: 0.1
        max_objs: 500
        min_radius: 2
        code_weights: [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2 ]
      test_cfg:
        post_center_limit_range: [ -61.2, -61.2, -10.0, 61.2, 61.2, 10.0 ]
        max_per_img: 500
        max_pool_nms: false
        min_radius: [ 4, 12, 10, 1, 0.85, 0.175 ]
        score_threshold: 0.1
        out_size_factor: 8
        voxel_size: ${voxel_size[:2]}
        nms_type:
          - circle
          - rotate
          - rotate
          - circle
          - rotate
          - rotate
        nms_scale:
          - [ 1.0 ]
          - [ 1.0, 1.0 ]
          - [ 1.0, 1.0 ]
          - [ 1.0 ]
          - [ 1.0, 1.0 ]
          - [ 2.5, 4.0 ]
        pre_max_size: 1000
        post_max_size: 83
        nms_thr: 0.2
      tasks:
        - [ "car" ]
        - [ "truck", "construction_vehicle" ]
        - [ "bus", "trailer" ]
        - [ "barrier" ]
        - [ "motorcycle", "bicycle" ]
        - [ "pedestrian", "traffic_cone" ]
      common_heads:
        reg: [ 2, 2 ]
        height: [ 1, 2 ]
        dim: [ 3, 2 ]
        rot: [ 2, 2 ]
        vel: [ 2, 2 ]
      share_conv_channel: 64
      bbox_coder:
        type: CenterPointBBoxCoder
        pc_range: ${point_cloud_range}
        post_center_range: [ -61.2, -61.2, -10.0, 61.2, 61.2, 10.0 ]
        max_num: 500
        score_threshold: 0.1
        out_size_factor: 8
        voxel_size: ${voxel_size[:2]}
        code_size: 9
      separate_head:
        type: SeparateHead
        init_bias: -2.19
        final_kernel: 3
      loss_cls:
        type: GaussianFocalLoss
        reduction: mean
      loss_bbox:
        type: L1Loss
        reduction: mean
        loss_weight: 0.25
      norm_bbox: true
    occ:
      type: BEVOCCHead2D
      in_dim: 128
      out_dim: 128
      Dz: 16
      use_mask: false
      num_classes: 18
      use_predicter: true
      class_balance: false
      loss_occ:
        type: CrossEntropyLoss
        use_sigmoid: false
        ignore_index: 255
        loss_weight: 1.0
        drop_free: true
      coordinate_transform:
        type: CrossCoordinateSample
        point_range: [-40., 40., -40., 40., 0., 0.]
        point_num: [200, 200, 1]
        lidar_point_range: [-54.0, 54.0, -54.0, 54.0]
        in_dim: 512
        out_dim: 128
  loss_scale:
    object: 0.01
    occ: 1.0

optimizer:
  type: AdamW
  lr: 2.0e-4
  weight_decay: 0.01

optimizer_config:
  grad_clip:
    max_norm: 35
    norm_type: 2

lr_config:
  policy: CosineAnnealing
  warmup: linear
  warmup_iters: 500
  warmup_ratio: 0.33333333
  min_lr_ratio: 1.0e-3

max_epochs: 6

evaluation:
  interval: 2

[AlphaPlusTT]

And I‘m curious about is there any other single Lidar Occ precetion model?

OccRWKV