One Step Closer: Creating the Future to Boost Monocular Semantic Scene Completion
This addresses a critical perception challenge for autonomous vehicles by improving occlusion reasoning in 3D scene completion, though it appears incremental as it builds on existing SSC methods with temporal enhancements.
The paper tackles the problem of monocular semantic scene completion in autonomous driving by proposing CF-SSC, a temporal framework that uses pseudo-future frame prediction to handle occlusions and limited field of view, achieving state-of-the-art performance on SemanticKITTI and SSCBench-KITTI-360 benchmarks.
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. Comprehensive 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.