Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Authors: Yurong You, Katie Z Luo, Xiangyu Chen, Junan Chen, Wei-Lun Chao, Wen Sun, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach on two large-scale, real-world self-driving datasets, Lyft Level 5 Perception (Kesten et al., 2019) and the nu Scenes Dataset (Caesar et al., 2020), with multiple representative modern object detection models (Lang et al., 2019; Yan et al., 2018; Zhou & Tuzel, 2018; Shi et al., 2019; 2020a) under various settings and show consistent performance gains. Our code is available at https://github.com/Yurong You/Hindsight. 4 EXPERIMENTS |
| Researcher Affiliation | Academia | 1Cornell University, Ithaca, NY 2The Ohio State University, Columbus, OH |
| Pseudocode | No | The paper describes the pipeline and components but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/Yurong You/Hindsight. |
| Open Datasets | Yes | We validate our approach, HINDSIGHT, on the Lyft Level 5 Perception Dataset (Kesten et al., 2019) (Lyft) and the nu Scenes Dataset (Caesar et al., 2020). |
| Dataset Splits | No | The paper states training/test splits (e.g., "12,407/2,274 training/test samples"), but it does not explicitly specify a separate validation dataset split. |
| Hardware Specification | Yes | All models are trained with 4 NVIDIA 3090 GPUs. |
| Software Dependencies | No | The paper mentions using "Minkowski Engine" and "Open PCDet" but does not provide specific version numbers for these software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | We set [ Hs, He], the range of scans that are combined to produce dense point clouds, to be [0, 20] m... We use default quantization size δ = 0.3 m for all the experiments... Due to GPU memory limit in training, we use Tmax = 5 available past traversals... The filter kernel size K is 5. The dimensionality of the history features dhistory is 64... We use most of the default hyper-parameters tuned for KITTI, with the exception that we enlarge the perception range from 70m to 90m... and we reduce the number of training epochs by 1/4 on Lyft. |