mlsanityData to 7 Oct 2026

SparseDet: Towards End-to-End 3D Object Detection

arXiv 2206.00960 ↗First posted 2 Jun 2022cs.CV cs.AIcited by 1

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose SparseDet for end-to-end 3D object detection from point cloud. Existing works on 3D object detection rely on dense object candidates over all locations in a 3D or 2D grid following the mainstream methods for object detection in 2D images. However, this dense paradigm requires expertise in data to fulfill the gap between label and detection. As a new detection paradigm, SparseDet maintains a fixed set of learnable proposals to represent latent candidates and directly perform classification and localization for 3D objects through stacked transformers. It demonstrates that effective 3D object detection can be achieved with none of post-processing such as redundant removal and non-maximum suppression. With a properly designed network, SparseDet achieves highly competitive detection accuracy while running with a more efficient speed of 34.5 FPS. We believe this end-to-end paradigm of SparseDet will inspire new thinking on the sparsity of 3D object detection.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 39 references that are in the map. First posted on arXiv 2 Jun 2022.

Builds on

33 of 39 references.

References · 33

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21 more
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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Diffusion-based 3D Object Detection with Random Boxescited by 5