GUIDE: Gaussian Unified Instance Detection for Enhanced Obstacle Perception in Autonomous Driving

TL;DR

GUIDE introduces a novel 3D Gaussian-based framework for obstacle detection and occupancy prediction in autonomous driving, significantly improving accuracy and efficiency over traditional methods.

Contribution

The paper presents GUIDE, a new approach using 3D Gaussians for instance detection and occupancy prediction, with robust tracking and sparse representation for real-world driving environments.

Findings

50% improvement in instance occupancy mAP over existing methods

Achieves competitive tracking capabilities

Establishes a new benchmark in autonomous perception

Abstract

In the realm of autonomous driving, accurately detecting surrounding obstacles is crucial for effective decision-making. Traditional methods primarily rely on 3D bounding boxes to represent these obstacles, which often fail to capture the complexity of irregularly shaped, real-world objects. To overcome these limitations, we present GUIDE, a novel framework that utilizes 3D Gaussians for instance detection and occupancy prediction. Unlike conventional occupancy prediction methods, GUIDE also offers robust tracking capabilities. Our framework employs a sparse representation strategy, using Gaussian-to-Voxel Splatting to provide fine-grained, instance-level occupancy data without the computational demands associated with dense voxel grids. Experimental validation on the nuScenes dataset demonstrates GUIDE's performance, with an instance occupancy mAP of 21.61, marking a 50\% improvement over existing methods, alongside competitive tracking capabilities. GUIDE establishes a new benchmark in autonomous perception systems, effectively combining precision with computational efficiency to better address the complexities of real-world driving environments.