LiDAR Place Recognition (LPR) is a key component in robotic localization, enabling robots to align current scans with prior maps of their environment. While Visual Place Recognition (VPR) has embraced Vision Foundation Models (VFMs) to enhance descriptor robustness, LPR has relied on task-specific models with limited use of pre-trained foundation-level knowledge. This is due to the lack of 3D foundation models and the challenges of using VFM with LiDAR point clouds. To tackle this, we introduce ImLPR, a novel pipeline that employs a pre-trained DINOv2 VFM to generate rich descriptors for LPR. To the best of our knowledge, ImLPR is the first method to utilize a VFM for LPR while retaining the majority of pre-trained knowledge. ImLPR converts raw point clouds into novel three-channel Range Image Views (RIV) to leverage VFM in the LiDAR domain. It employs MultiConv adapters and Patch-InfoNCE loss for effective feature learning. We validate ImLPR on public datasets and outperform state-of-the-art (SOTA) methods across multiple evaluation metrics in both intra- and inter-session LPR. Comprehensive ablations on key design choices such as channel composition, RIV, adapters, and the patch-level loss quantify each component's impact. We release ImLPR as open source for the robotics community: https://github.com/minwoo0611/ImLPR.