diff --git a/pcdet/models/backbones_3d/spconv_backbone_2d.py b/pcdet/models/backbones_3d/spconv_backbone_2d.py
new file mode 100644
index 0000000..3784ada
--- /dev/null
+++ b/pcdet/models/backbones_3d/spconv_backbone_2d.py
@@ -0,0 +1,300 @@
+from functools import partial
+
+import torch.nn as nn
+
+from ...utils.spconv_utils import replace_feature, spconv
+
+
+def post_act_block(in_channels, out_channels, kernel_size, indice_key=None, stride=1, padding=0,
+                   conv_type='subm', norm_fn=None):
+
+    if conv_type == 'subm':
+        conv = spconv.SubMConv2d(in_channels, out_channels, kernel_size, bias=False, indice_key=indice_key)
+    elif conv_type == 'spconv':
+        conv = spconv.SparseConv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding,
+                                   bias=False, indice_key=indice_key)
+    elif conv_type == 'inverseconv':
+        conv = spconv.SparseInverseConv2d(in_channels, out_channels, kernel_size, indice_key=indice_key, bias=False)
+    else:
+        raise NotImplementedError
+
+    m = spconv.SparseSequential(
+        conv,
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+def post_act_block_dense(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, norm_fn=None):
+    m = nn.Sequential(
+        nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=padding, dilation=dilation, bias=False),
+        norm_fn(out_channels),
+        nn.ReLU(),
+    )
+
+    return m
+
+
+class SparseBasicBlock(spconv.SparseModule):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None, indice_key=None):
+        super(SparseBasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = spconv.SubMConv2d(
+            inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = spconv.SubMConv2d(
+            planes, planes, kernel_size=3, stride=stride, padding=1, bias=bias, indice_key=indice_key
+        )
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = replace_feature(out, self.bn1(out.features))
+        out = replace_feature(out, self.relu(out.features))
+
+        out = self.conv2(out)
+        out = replace_feature(out, self.bn2(out.features))
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = replace_feature(out, out.features + identity.features)
+        out = replace_feature(out, self.relu(out.features))
+
+        return out
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, norm_fn=None, downsample=None):
+        super(BasicBlock, self).__init__()
+
+        assert norm_fn is not None
+        bias = norm_fn is not None
+        self.conv1 = nn.Conv2d(inplanes, planes, 3, stride=stride, padding=1, bias=bias)
+        self.bn1 = norm_fn(planes)
+        self.relu = nn.ReLU()
+        self.conv2 = nn.Conv2d(planes, planes, 3, stride=stride, padding=1, bias=bias)
+        self.bn2 = norm_fn(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out = out + identity
+        out = self.relu(out)
+
+        return out
+    
+    
+class PillarBackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+        self.sparse_shape = grid_size[[1, 0]]
+        
+        block = post_act_block
+        dense_block = post_act_block_dense
+
+        self.conv1 = spconv.SparseSequential(
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+            block(32, 32, 3, norm_fn=norm_fn, padding=1, indice_key='subm1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408] <- [800, 704]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+            block(64, 64, 3, norm_fn=norm_fn, padding=1, indice_key='subm2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704] <- [400, 352]
+            block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            block(128, 128, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+            block(128, 128, 3, norm_fn=norm_fn, padding=1, indice_key='subm3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(128, 256, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv4', conv_type='spconv'),
+            block(256, 256, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+            block(256, 256, 3, norm_fn=norm_fn, padding=1, indice_key='subm4'),
+        )
+        
+        norm_fn = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)
+        self.conv5 = nn.Sequential(
+            # [200, 176] <- [100, 88]
+            dense_block(256, 256, 3, norm_fn=norm_fn, stride=2, padding=1),
+            dense_block(256, 256, 3, norm_fn=norm_fn, padding=1),
+            dense_block(256, 256, 3, norm_fn=norm_fn, padding=1),
+        )
+        
+        self.num_point_features = 256
+        self.backbone_channels = {
+            'x_conv1': 32,
+            'x_conv2': 64,
+            'x_conv3': 128,
+            'x_conv4': 256,
+            'x_conv5': 256
+        }
+
+
+    def forward(self, batch_dict):
+        pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=pillar_features,
+            indices=pillar_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        
+        x_conv1 = self.conv1(input_sp_tensor)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+        x_conv4 = x_conv4.dense()
+        x_conv5 = self.conv5(x_conv4)
+
+        batch_dict.update({
+            'multi_scale_2d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+                'x_conv5': x_conv5,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_2d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+                'x_conv5': 16,
+            }
+        })
+
+        return batch_dict
+
+
+class PillarRes18BackBone8x(nn.Module):
+    def __init__(self, model_cfg, input_channels, grid_size, **kwargs):
+        super().__init__()
+        self.model_cfg = model_cfg
+        norm_fn = partial(nn.BatchNorm1d, eps=1e-3, momentum=0.01)
+        self.sparse_shape = grid_size[[1, 0]]
+        
+        block = post_act_block
+        dense_block = post_act_block_dense
+        
+        self.conv1 = spconv.SparseSequential(
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+            SparseBasicBlock(32, 32, norm_fn=norm_fn, indice_key='res1'),
+        )
+
+        self.conv2 = spconv.SparseSequential(
+            # [1600, 1408] <- [800, 704]
+            block(32, 64, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv2', conv_type='spconv'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+            SparseBasicBlock(64, 64, norm_fn=norm_fn, indice_key='res2'),
+        )
+
+        self.conv3 = spconv.SparseSequential(
+            # [800, 704] <- [400, 352]
+            block(64, 128, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv3', conv_type='spconv'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+            SparseBasicBlock(128, 128, norm_fn=norm_fn, indice_key='res3'),
+        )
+
+        self.conv4 = spconv.SparseSequential(
+            # [400, 352] <- [200, 176]
+            block(128, 256, 3, norm_fn=norm_fn, stride=2, padding=1, indice_key='spconv4', conv_type='spconv'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+            SparseBasicBlock(256, 256, norm_fn=norm_fn, indice_key='res4'),
+        )
+        
+        norm_fn = partial(nn.BatchNorm2d, eps=1e-3, momentum=0.01)
+        self.conv5 = nn.Sequential(
+            # [200, 176] <- [100, 88]
+            dense_block(256, 256, 3, norm_fn=norm_fn, stride=2, padding=1),
+            BasicBlock(256, 256, norm_fn=norm_fn),
+            BasicBlock(256, 256, norm_fn=norm_fn),
+        )
+
+        self.num_point_features = 256
+        self.backbone_channels = {
+            'x_conv1': 32,
+            'x_conv2': 64,
+            'x_conv3': 128,
+            'x_conv4': 256,
+            'x_conv5': 256
+        }
+
+    def forward(self, batch_dict):
+        pillar_features, pillar_coords = batch_dict['pillar_features'], batch_dict['pillar_coords']
+        batch_size = batch_dict['batch_size']
+        input_sp_tensor = spconv.SparseConvTensor(
+            features=pillar_features,
+            indices=pillar_coords.int(),
+            spatial_shape=self.sparse_shape,
+            batch_size=batch_size
+        )
+        
+        x_conv1 = self.conv1(input_sp_tensor)
+        x_conv2 = self.conv2(x_conv1)
+        x_conv3 = self.conv3(x_conv2)
+        x_conv4 = self.conv4(x_conv3)
+        x_conv4 = x_conv4.dense()
+        x_conv5 = self.conv5(x_conv4)
+
+        # batch_dict.update({
+        #     'encoded_spconv_tensor': out,
+        #     'encoded_spconv_tensor_stride': 8
+        # })
+        batch_dict.update({
+            'multi_scale_2d_features': {
+                'x_conv1': x_conv1,
+                'x_conv2': x_conv2,
+                'x_conv3': x_conv3,
+                'x_conv4': x_conv4,
+                'x_conv5': x_conv5,
+            }
+        })
+        batch_dict.update({
+            'multi_scale_2d_strides': {
+                'x_conv1': 1,
+                'x_conv2': 2,
+                'x_conv3': 4,
+                'x_conv4': 8,
+                'x_conv5': 16,
+            }
+        })
+        
+        return batch_dict