  The strong demand of autonomous driving in the industry has lead to strong
interest in 3D object detection and resulted in many excellent 3D object
detection algorithms. However, the vast majority of algorithms only model
single-frame data, ignoring the temporal information of the sequence of data.
In this work, we propose a new transformer, called Temporal-Channel
Transformer, to model the spatial-temporal domain and channel domain
relationships for video object detecting from Lidar data. As a special design
of this transformer, the information encoded in the encoder is different from
that in the decoder, i.e. the encoder encodes temporal-channel information of
multiple frames while the decoder decodes the spatial-channel information for
the current frame in a voxel-wise manner. Specifically, the temporal-channel
encoder of the transformer is designed to encode the information of different
channels and frames by utilizing the correlation among features from different
channels and frames. On the other hand, the spatial decoder of the transformer
will decode the information for each location of the current frame. Before
conducting the object detection with detection head, the gate mechanism is
deployed for re-calibrating the features of current frame, which filters out
the object irrelevant information by repetitively refine the representation of
target frame along with the up-sampling process. Experimental results show that
we achieve the state-of-the-art performance in grid voxel-based 3D object
detection on the nuScenes benchmark.
