Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
Authors: Yurong You, Yan Wang, Wei-Lun Chao, Divyansh Garg, Geoff Pleiss, Bharath Hariharan, Mark Campbell, Kilian Q. Weinberger
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show on the KITTI object detection benchmark that our combined approach yields substantial improvements in depth estimation and stereo-based 3D object detection outperforming the previous state-of-the-art detection accuracy for faraway objects by 40%. Our code is available at https://github.com/mileyan/Pseudo_Lidar_V2. We conduct extensive empirical studies of our approaches on the KITTI object detection benchmark (Geiger et al., 2012; 2013) and achieve remarkable results. |
| Researcher Affiliation | Academia | Yurong You 1, Yan Wang 1, Wei-Lun Chao 2, Divyansh Garg1, Geoff Pleiss1, Bharath Hariharan1, Mark Campbell1, and Kilian Q. Weinberger1 1Cornell University, Ithaca, NY 2The Ohio State University, Columbus, OH {yy785, yw763, dg595, gp346, bh497, mc288, kqw4}@cornell.edu chao.209@osu.edu |
| Pseudocode | Yes | Algorithm 1: Graph-based depth correction (GDC). |
| Open Source Code | Yes | Our code is available at https://github.com/mileyan/Pseudo_Lidar_V2. |
| Open Datasets | Yes | We evaluate on the KITTI dataset (Geiger et al., 2013; 2012), which contains 7,481 and 7,518 images for training and testing. We follow (Chen et al., 2015) to separate the 7,481 images into 3,712 for training and 3,769 validation. |
| Dataset Splits | Yes | We follow (Chen et al., 2015) to separate the 7,481 images into 3,712 for training and 3,769 validation. |
| Hardware Specification | No | With simple optimizations, GDC runs in 90 ms/frame using a single GPU (7.7 ms for KD-tree construction and search). |
| Software Dependencies | No | We use PSMNET (Chang & Chen, 2018) as the backbone for our stereo depth estimation network (SDN). We applied the grid_sample function in Py Torch for bilinear interpolation. |
| Experiment Setup | Yes | For GDC we set k = 10 and consider adding signal from a (simulated) 4-beam Li DAR, unless stated otherwise. We train PIXOR using RMSProp with momentum 0.9, learning rate 10^-5 (decay by 10 after 50 and 80 epochs) for 90 epochs. |