Teaching Cars to See From Above
What if autonomous vehicles could see the road the way a bird does?
Cars already see the road — but seeing it like a bird sees it remains one of the hardest problems in autonomous driving.
The Science
What They Found
Why This Changes Things
What's Next
TVB is organized into two distinct stages, as depicted in Fig. 2. In the first stage, CFLOW learns and produces BEV representations, yielding several candidate outputs. In the subsequent stage, these candidate BEV representations are processed by the adaptive fusion module, which generates the final BEV segmentation.
During the training phase, we first fuse camera images from multiple views into BEV features x using cross-attention. Subsequently, x is fed into the encoder of the posterior network along with the ground truth c of the BEV to obtain latent codes containing both types of information. At the same time, x is fed into another prior network to obtain latent codes containing x information. The KL divergence is then used to assess the similarity between the two codes. Multiple sampling is then performed on the latent codes from the prior network, which are then fed into the decoder to reconstruct of BEV maps. Finally, the BAF module is used to fuse multiple candidate BEV maps to obtain the BEV segmentation.
The Science
What They Found
Why This Changes Things
What's Next
TVB is organized into two distinct stages, as depicted in Fig. 2. In the first stage, CFLOW learns and produces BEV representations, yielding several candidate outputs. In the subsequent stage, these candidate BEV representations are processed by the adaptive fusion module, which generates the final BEV segmentation.
During the training phase, we first fuse camera images from multiple views into BEV features x using cross-attention. Subsequently, x is fed into the encoder of the posterior network along with the ground truth c of the BEV to obtain latent codes containing both types of information. At the same time, x is fed into another prior network to obtain latent codes containing x information. The KL divergence is then used to assess the similarity between the two codes. Multiple sampling is then performed on the latent codes from the prior network, which are then fed into the decoder to reconstruct of BEV maps. Finally, the BAF module is used to fuse multiple candidate BEV maps to obtain the BEV segmentation.
The Science
What They Found
Why This Changes Things
What's Next
TVB is organized into two distinct stages, as depicted in Fig. 2. In the first stage, CFLOW learns and produces BEV representations, yielding several candidate outputs. In the subsequent stage, these candidate BEV representations are processed by the adaptive fusion module, which generates the final BEV segmentation.
During the training phase, we first fuse camera images from multiple views into BEV features x using cross-attention. Subsequently, x is fed into the encoder of the posterior network along with the ground truth c of the BEV to obtain latent codes containing both types of information. At the same time, x is fed into another prior network to obtain latent codes containing x information. The KL divergence is then used to assess the similarity between the two codes. Multiple sampling is then performed on the latent codes from the prior network, which are then fed into the decoder to reconstruct of BEV maps. Finally, the BAF module is used to fuse multiple candidate BEV maps to obtain the BEV segmentation.
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