Sparse active illumination enables precise time-of-flight depth sensing as it
maximizes signal-to-noise ratio for low power budgets. However, depth
completion is required to produce dense depth maps for 3D perception. We
address this task with realistic illumination and sensor resolution constraints
by simulating ToF datasets for indoor 3D perception with challenging sparsity
levels. We propose a quantized convolutional encoder-decoder network for this
task. Our model achieves optimal depth map quality by means of input
pre-processing and carefully tuned training with a geometry-preserving loss
function. We also achieve low memory footprint for weights and activations by
means of mixed precision quantization-at-training techniques. The resulting
quantized models are comparable to the state of the art in terms of quality,
but they require very low GPU times and achieve up to 14-fold memory size
reduction for the weights w.r.t. their floating point counterpart with minimal
impact on quality metrics.

提出了一种适用于室内 3D 感知的稀疏主动照明时间飞行（ToF）深度图修复方法。通过使用一个经过量化的卷积编码器 - 解码器网络，以及对输入进行预处理和使用几何保持损失函数进行精心调整的训练，该方法可以在实际光照和传感器分辨率约束下，生成具有高质量的深度图，同时仅需低 GPU 时间和内存占用。这种采用混合精度量化技术的模型可与目前模型的质量相当，并且在权重内存大小方面实现了最多 14 倍的减少，而对质量指标的影响也极小。