  Transfer learning is widely used in computer vision (CV), natural language
processing (NLP) and achieves great success. Most transfer learning systems are
based on the same modality (e.g. RGB image in CV and text in NLP). However, the
cross-modality transfer learning (CMTL) systems are scarce. In this work, we
study CMTL from 2D to 3D sensor to explore the upper bound performance of 3D
sensor only systems, which play critical roles in robotic navigation and
perform well in low light scenarios. While most CMTL pipelines from 2D to 3D
vision are complicated and based on Convolutional Neural Networks (ConvNets),
ours is easy to implement, expand and based on both ConvNets and Vision
transformers(ViTs): 1) By converting point clouds to pseudo-images, we can use
an almost identical network from pre-trained models based on 2D images. This
makes our system easy to implement and expand. 2) Recently ViTs have been
showing good performance and robustness to occlusions, one of the key reasons
for poor performance of 3D vision systems. We explored both ViT and ConvNet
with similar model sizes to investigate the performance difference. We name our
approach simCrossTrans: simple cross-modality transfer learning with ConvNets
or ViTs. Experiments on SUN RGB-D dataset show: with simCrossTrans we achieve
$13.2\%$ and $16.1\%$ absolute performance gain based on ConvNets and ViTs
separately. We also observed the ViTs based performs $9.7\%$ better than the
ConvNets one, showing the power of simCrossTrans with ViT. simCrossTrans with
ViTs surpasses the previous state-of-the-art (SOTA) by a large margin of
$+15.4\%$ mAP50. Compared with the previous 2D detection SOTA based RGB images,
our depth image only system only has a $1\%$ gap. The code, training/inference
logs and models are publicly available at
https://github.com/liketheflower/simCrossTrans
