RIDE: Relocalization-Informed Depth Estimation with 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2609.11079v1
• Date: Thu, 10 Sep 2026 04:36:58 GMT
• Title: RIDE: Relocalization-Informed Depth Estimation with 3D Gaussian Splatting
• Abstract summary: We present RIDE, which estimates dense depth from a robot's RGB map.<n>Experiments show improved depth accuracy temporal consistency scale-only calibration, demonstrating how dense geometry can support both pose recovery and robot perception.
• Score: 20.29273985020268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Render--match--PnP relocalization establishes correspondences between query image pixels and 3D map points for camera pose recovery, but their potential to support dense depth estimation is often overlooked. To exploit this geometric information, we present RIDE, which estimates dense metric depth from a robot's RGB stream. Given a metrically scaled 3D Gaussian Splatting (3DGS) model, RIDE combines sparse metric depth observations derived from PnP-RANSAC inlier correspondences with the geometric prior of a pretrained video-depth model. To handle uneven and intermittent observations, it integrates global and local depth correction with temporal memory, supporting depth estimation through short observation gaps after metric scale initialization. Trained on public RGB-D videos, RIDE is evaluated on robot sequences without fine tuning. Experiments show improved depth accuracy and temporal consistency over scale-only calibration, demonstrating how localization geometry can support both pose recovery and dense robot perception.

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