SST
Code for a series of work in LiDAR perception, including SST (CVPR 22), FSD (NeurIPS 22), FSD++ (TPAMI 23), FSDv2, and CTRL (ICCV 23, oral). (by tusen-ai)
3d-multi-resolution-rcnn
Official PyTorch implementaiton of the paper "3D Instance Segmentation Framework for Cerebral Microbleeds using 3D Multi-Resolution R-CNN." (by arthur801031)
SST | 3d-multi-resolution-rcnn | |
---|---|---|
1 | 1 | |
742 | 25 | |
2.3% | - | |
6.7 | 0.0 | |
7 months ago | over 3 years ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SST
Posts with mentions or reviews of SST.
We have used some of these posts to build our list of alternatives
and similar projects.
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Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection
This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. They first label the objects with clear shapes in a track, and then leverage the temporal coherence to infer the annotations of obscure objects. Drawing inspiration from this, we propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective. Our method features a bidirectional tracking module and a track-centric learning module. Such a design allows our detector to infer and refine a complete track once the object is detected at a certain moment. We refer to this characteristic as "onCe detecTed, neveR Lost" and name the proposed system CTRL. Extensive experiments demonstrate the remarkable performance of our method, surpassing the human-level annotating accuracy and the previous state-of-the-art methods in the highly competitive Waymo Open Dataset without model ensemble. The code will be made publicly available at https://github.com/tusen-ai/SST.
3d-multi-resolution-rcnn
Posts with mentions or reviews of 3d-multi-resolution-rcnn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-09-07.
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3D rcnn
Is this what you're looking for: https://github.com/arthur801031/3d-multi-resolution-rcnn
What are some alternatives?
When comparing SST and 3d-multi-resolution-rcnn you can also consider the following projects:
nnUNet
mmdetection3d - OpenMMLab's next-generation platform for general 3D object detection.
sahi - Framework agnostic sliced/tiled inference + interactive ui + error analysis plots
torchio - Medical imaging toolkit for deep learning
mmocr - OpenMMLab Text Detection, Recognition and Understanding Toolbox
medicaldetectiontoolkit - The Medical Detection Toolkit contains 2D + 3D implementations of prevalent object detectors such as Mask R-CNN, Retina Net, Retina U-Net, as well as a training and inference framework focused on dealing with medical images.
EPro-PnP - [CVPR 2022 Oral, Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation