We propose a method to infer a dense depth map from a single image, its
calibration, and the associated sparse point cloud. In order to leverage
existing models (teachers) that produce putative depth maps, we propose an
adaptive knowledge distillation approach that yields a positive congruent
training process, wherein a student model avoids learning the error modes of
the teachers. In the absence of ground truth for model selection and training,
our method, termed Monitored Distillation, allows a student to exploit a blind
ensemble of teachers by selectively learning from predictions that best
minimize the reconstruction error for a given image. Monitored Distillation
yields a distilled depth map and a confidence map, or ``monitor'', for how well
a prediction from a particular teacher fits the observed image. The monitor
adaptively weights the distilled depth where if all of the teachers exhibit
high residuals, the standard unsupervised image reconstruction loss takes over
as the supervisory signal. On indoor scenes (VOID), we outperform blind
ensembling baselines by 17.53% and unsupervised methods by 24.25%; we boast a
79% model size reduction while maintaining comparable performance to the best
supervised method. For outdoors (KITTI), we tie for 5th overall on the
benchmark despite not using ground truth. Code available at:
this https URL.

提出采用可适应的知识蒸馏方法结合无监督训练和盲集成学习的 Monitored Distillation 方法，能在不存在地面真值数据的情况下，从单张图片、相应的稀疏点云和内置集成模型中获取稠密的深度信息，且在 VOID 室内场景数据集上比盲集成基线算法效果提高了 17.53%，比无监督方法效果提高了 24.25%，且比最好的有监督方法保持可比性。