  We present here, a novel network architecture called MergeNet for discovering
small obstacles for on-road scenes in the context of autonomous driving. The
basis of the architecture rests on the central consideration of training with
less amount of data since the physical setup and the annotation process for
small obstacles is hard to scale. For making effective use of the limited data,
we propose a multi-stage training procedure involving weight-sharing, separate
learning of low and high level features from the RGBD input and a refining
stage which learns to fuse the obtained complementary features. The model is
trained and evaluated on the Lost and Found dataset and is able to achieve
state-of-art results with just 135 images in comparison to the 1000 images used
by the previous benchmark. Additionally, we also compare our results with
recent methods trained on 6000 images and show that our method achieves
comparable performance with only 1000 training samples.
