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
this https URL

提出了一种名为 MonoOcc 的方法，通过在框架的浅层引入辅助语义损失作为监督和利用图像条件下的交叉注意力模块来改进单目占据预测框架，并利用较低的硬件成本将时间信息和更丰富的知识从更大的图像主干传输到单目语义占据预测框架，从而取得了基于相机的 SemanticKITTI 场景完成基准测试中的最佳性能。