This is a novel average precision calculation named hybrid N-point interpolation method to eliminate the average precision distortion in KITTI 3D Object Detection Benchmark.
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Updated
Apr 2, 2023 - Python
This is a novel average precision calculation named hybrid N-point interpolation method to eliminate the average precision distortion in KITTI 3D Object Detection Benchmark.
A comprehensive PyTorch framework for Semi-Supervised 3D Object Detection using LiDAR Point Clouds
Python tools for working with KITTI data.
Object Detection Dataset Format Converter
3D LiDAR Semantic Segmentation with range images and Retentive Networks
Annotation File Converter is a GitHub repository that includes Python-based conversion scripts to convert annotations from one format to another.
Train a Fully Convolutional Network to find roads from images!
Practicing Pytorch and Studying Depth Estimation
SYDE673 A3. Integrate RFNet (receptive field CNN) in PySLAMv2 for VO and SLAM
Library to load various vision datasets from disk
Implementation of SECOND in PyTorch for KITTI 3D Object Detetcion
Project: Generating overhead birds-eye-view occupancy grid map with semantic information from lidar and camera data.
(Semi based Modified version) Virtual Sparse Convolution for Multimodal 3D Object Detection
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