Search Results for author:
Found 8 papers, 3 papers with code
Scalable Primitives for Generalized Sensor Fusion in Autonomous Vehicles
This paves the way for the industry to jointly design hardware and software architectures as well as large fleets with heterogeneous configurations.
SafetyNet: Safe planning for real-world self-driving vehicles using machine-learned policies
To combat this, our approach uses a simple yet effective rule-based fallback layer that performs sanity checks on an ML planner's decisions (e. g. avoiding collision, assuring physical feasibility).
Recurrent Slice Networks for 3D Segmentation of Point Clouds
The key component of the RSNet is a lightweight local dependency module.
Ranked #46 on
Semantic Segmentation
on S3DIS
Learning to Prune Filters in Convolutional Neural Networks
Specifically, we introduce a "try-and-learn" algorithm to train pruning agents that remove unnecessary CNN filters in a data-driven way.
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results.
Ranked #1 on
3D Semantic Instance Segmentation
on ScanNetV1
Shape Inpainting using 3D Generative Adversarial Network and Recurrent Convolutional Networks
The 3D-ED-GAN is a 3D convolutional neural network trained with a generative adversarial paradigm to fill missing 3D data in low-resolution.
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
In this paper, we propose an automatic and fast algorithm to localize and label the vertebra centroids in 3D CT volumes.
Scene Labeling using Gated Recurrent Units with Explicit Long Range Conditioning
A novel neural network architecture is built for scene labeling tasks where one of the variants of the new RNN unit, Gated Recurrent Unit with Explicit Long-range Conditioning (GRU-ELC), is used to model multi scale contextual dependencies in images.
