Huawei Technologies Co., Limited

Ryan Razani

Bingbing Liu

Ran Cheng

Ehsan Taghavi

Vahid Partovi Nia

Devices, systems, methods, and media are disclosed for performing an object detection task comprising: obtaining a semantic segmentation map representing a real-world space, the semantic segmentation map including an array of elements that each represent a respective location in the real-world space and are assigned a respective element classification label; clustering groups of the elements based on the assigned respective element classification labels to identify at least a first cluster of elements that have each been assigned the same respective element classification label; generating, based on a location of the first cluster within the semantic segmentation map, at least one anchor that defines a respective probable object location of a first dynamic object; and generating, based on the semantic segmentation map and the at least one anchor, a respective bounding box and object instance classification label for the first dynamic object.

Systems and methods for enhancement of 3D object detection using point cloud semantic segmentation and attentive anchor generation

A method and processing unit for training a neural network to selectively quantize weights of a filter of the neural network as either binary weights or ternary weights. A plurality of training iterations a performed that each comprise: quantizing a set of real-valued weights of a filter to generate a corresponding set of quantized weights; generating an output feature tensor based on matrix multiplication of an input feature tensor and the set of quantized weights; computing, based on the output feature tensor, a loss based on a regularization function that is configured to move the loss towards a minimum value when either: (i) the quantized weights move towards binary weights, or (ii) the quantized weights move towards a ternary weights; computing a gradient with an objective of minimizing the loss; updating the real-valued weights based on the computed gradient. When the training iterations are complete, a set of weights quantized from the updated real-valued weights is stored as either a set of binary weights or a set of ternary weights.

Training method for quantizing the weights and inputs of a neural network

Systems, methods and apparatus for sematic segmentation of 3D point clouds using deep neural networks. The deep neural network generally has two primary subsystems: a multi-branch cascaded subnetwork that includes an encoder and a decoder, and is configured to receive a sparse 3D point cloud, and capture and fuse spatial feature information in the sparse 3D point cloud at multiple scales and multi hierarchical levels; and a spatial feature transformer subnetwork that is configured to transform the cascaded features generated by the multi-branch cascaded subnetwork and fuse these scaled features using a shared decoder attention framework to assist in the prediction of sematic classes for the sparse 3D point cloud.

Methods and systems for semantic segmentation of a point cloud

In methods and systems for graph-based panoptic segmentation of point clouds, points of a point cloud are received with a semantic label from a first category. Further, a plurality of unified cluster feature vectors from a second category are received, each being extracted from a cluster of points in the point cloud. Nodes of a constructed graph represent the unified feature vectors, and edges indicate the relationship between pairs of nodes. The edges are represented as an adjacency matrix indicating the existence or absence of an edge between pairs of nodes. A graph convolutional neural network uses the graph to predict an instance label for each node or an attribute for each edge, wherein the attribute of each edge is used for assigning the instance label to each node.

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Toronto, Canada

Ryan Razani