Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Pseudo-LiDAR++

Pseudo-LiDAR++

Reported on 6 benchmarks across 6 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology4 results

3DonKITTI Cars Moderate
AP75· 2019-06-14
42.43
best: 67.37 (DSGN++)
SOTA
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving arXiv:1906.06310
2D ClassificationonKITTI Cars Moderate
AP75· 2019-06-14
42.43
best: 67.37 (DSGN++)
SOTA
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving arXiv:1906.06310
2D Object DetectiononKITTI Cars Moderate
AP75· 2019-06-14
42.43
best: 67.37 (DSGN++)
SOTA
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving arXiv:1906.06310
16konKITTI Cars Moderate
AP75· 2019-06-14
42.43
best: 67.37 (DSGN++)
SOTA
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving arXiv:1906.06310

Computer Vision2 results

Object DetectiononKITTI Cars Moderate
AP75· 2019-06-14
42.43
best: 67.37 (DSGN++)
SOTA
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving arXiv:1906.06310
3D Object DetectiononKITTI Cars Moderate
AP75· 2019-06-14
42.43
best: 67.37 (DSGN++)
SOTA
Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving arXiv:1906.06310