MonoOcc: Digging into Monocular Semantic Occupancy Prediction
Yupeng Zheng Xiang Li Pengfei Li Yuhang Zheng Bu Jin Chengliang Zhong Xiaoxiao Long Hao Zhao Qichao Zhang
Monocular Semantic Occupancy Prediction aims to infer the complete 3D
geometry and semantic information of scenes from only 2D images. It has
garnered significant attention, particularly due to its potential to enhance
the 3D perception of autonomous vehicles. However, existing methods rely on a
complex cascaded framework with relatively limited information to restore 3D
scenes, including a dependency on supervision solely on the whole network's
output, single-frame input, and the utilization of a small backbone. These
challenges, in turn, hinder the optimization of the framework and yield
inferior prediction results, particularly concerning smaller and long-tailed
objects. To address these issues, we propose MonoOcc. In particular, we (i)
improve the monocular occupancy prediction framework by proposing an auxiliary
semantic loss as supervision to the shallow layers of the framework and an
image-conditioned cross-attention module to refine voxel features with visual
clues, and (ii) employ a distillation module that transfers temporal
information and richer knowledge from a larger image backbone to the monocular
semantic occupancy prediction framework with low cost of hardware. With these
advantages, our method yields state-of-the-art performance on the camera-based
SemanticKITTI Scene Completion benchmark. Codes and models can be accessed at
https://github.com/ucaszyp/MonoOcc