UNION: Unsupervised 3D Object Detection using Object Appearance-based Pseudo-Classes
Authors: Ted Lentsch, Holger Caesar, Dariu Gavrila
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on the nu Scenes dataset and increase the state-of-the-art performance for unsupervised 3D object discovery, i.e. UNION more than doubles the average precision to 38.4. |
| Researcher Affiliation | Academia | Ted Lentsch Holger Caesar Dariu M. Gavrila Department of Cognitive Robotics Delft University of Technology |
| Pseudocode | No | The paper describes the method pipeline in text and a figure (Figure 1), but does not include a formal pseudocode or algorithm block. |
| Open Source Code | Yes | The code is available at github.com/Ted Lentsch/UNION. |
| Open Datasets | Yes | Dataset. We evaluate our method on the challenging nu Scenes [3] dataset. This is a large-scale autonomous driving dataset for 3D perception captured in diverse weather and lighting conditions across Boston and Singapore. |
| Dataset Splits | Yes | It consists of 700, 150, and 150 scenes for training, validation, and testing, respectively. |
| Hardware Specification | Yes | We used 8 NVIDIA V100 32 Gi B GPUs for conducting the experiments. |
| Software Dependencies | No | The paper mentions software components like MMDetection3D, Center Point, DINOv2, and ViT-L/14, but does not provide specific version numbers for these software dependencies, only their names and general configurations. |
| Experiment Setup | Yes | More specifically, we use Center Point with pillars of 0.2 m as voxel encoder, do not use test time augmentation, and train for 20 epochs with a batch size of 4. All class-agnostic experiments are done without class-balanced grouping and sampling (CBGS) [34], while we do use CBGS for multi-class experiments to improve the performance of tail classes. |