Lu, HaoAng (Xi'an Jiaotong University), Su, Yuanqi (Xi'an Jiaotong University), Zhang, Xiaoning (Xi’an Jiaotong University), Hu, Hao (China Academy of Railway Sciences Corporation Limited)

One Step Closer: Creating the Future to Boost Monocular Semantic Scene Completion

Scheduled for presentation during the Regular Session "S28b-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (TH-EA-T28), Thursday, November 20, 2025, 14:30−14:50, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

In recent years, visual 3D Semantic Scene Completion (SSC) has emerged as a critical perception task for autonomous driving due to its ability to infer complete 3D scene layouts and semantics from single 2D images. However, in real-world traffic scenarios, a significant portion of the scene remains occluded or outside the camera's field of view - a fundamental challenge that existing monocular SSC methods fail to address adequately.

To overcome these limitations, we propose Creating the Future SSC(CF-SSC), a novel temporal SSC framework that leverages pseudo-future frame prediction to expand the model‘s effective perceptual range. Our approach combines poses and depths to establish accurate 3D correspondences, enabling geometrically-consistent fusion of past, present, and predicted future frames in 3D space. Unlike conventional methods that rely on simple feature stacking, our 3D-aware architecture achieves more robust scene completion by explicitly modeling spatial-temporal relationships.

Extensive experiments on SemanticKITTI and SSCBench-KITTI-360 benchmarks demonstrate state-of-the-art performance, validating the effectiveness of our approach, highlighting our method's ability to improve occlusion reasoning and 3D scene completion accuracy.