To address the non-uniform spatial distribution of pseudo point clouds, where point density is excessively high at close range but remains relatively sparse at long range, the resulting distance-dependent disparity can cause inconsistent proposal feature representations across different ranges and consequently degrade overall detection performance. To mitigate this issue, we introduce two components, namely geometric enhancement module(GEM) and adaptive region-of-interest attention fusion (ARAF) module. In GEM, a region-wise resampling strategy is adopted: voxel-level downsampling is applied to near-range regions to remove redundant points and suppress noise, while the sparse point distribution in far-range regions is preserved to maintain essential geometric structures. In ARAF, region importance is modeled at the region of interest (RoI) level by predicting adaptive weights and performing weighted fusion of features from real point clouds and pseudo point clouds. This design enables the network to dynamically emphasize far-range point cloud information according to object distance, thereby alleviating performance degradation caused by insufficient feature expressiveness in long-range scenarios. Experiments on the KITTI benchmark show that the proposed method improves the three evaluation metrics by 3.19%, 1.90% and 1.75%, respectively.