Scene segmentation is the task of splitting a scene into its various object components.
Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.
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We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.
Point cloud is an important type of geometric data structure.
Ranked #2 on Scene Segmentation on ScanNet
Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively.
Ranked #5 on Semantic Segmentation on COCO-Stuff test
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on Medical Image Segmentation on RITE
Furthermore, these locations are continuous in space and can be learned by the network.
Ranked #1 on 3D Part Segmentation on ShapeNet-Part
The computation cost and memory footprints of the voxel-based models grow cubically with the input resolution, making it memory-prohibitive to scale up the resolution.
Ranked #1 on 3D Object Detection on KITTI Pedestrian Easy val
By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.
Ranked #1 on Scene Segmentation on SUN-RGBD