# Finding Your (3D) Center: 3D Object Detection Using a Learned Loss

arXiv: https://arxiv.org/abs/2004.02693 · HTML: https://mlsanity.com/paper/2004.02693/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 5 of 5)

- [FGR: Frustum-Aware Geometric Reasoning for Weakly Supervised 3D Vehicle Detection](https://mlsanity.com/paper/2105.07647/) (2021)
- [Diff3DETR:Agent-based Diffusion Model for Semi-supervised 3D Object Detection](https://mlsanity.com/paper/2408.00286/) (2024)
- [GrabS: Generative Embodied Agent for 3D Object Segmentation without Scene Supervision](https://mlsanity.com/paper/2504.11754/) (2025)
- [EvObj: Learning Evolving Object-centric Representations for 3D Instance Segmentation without Scene Supervision](https://mlsanity.com/paper/2605.13152/) (2026)
- [FoundObj: Self-supervised Foundation Models as Rewards for Label-free 3D Object Segmentation](https://mlsanity.com/paper/2605.27178/) (2026)

## Abstract (arXiv)

Massive semantically labeled datasets are readily available for 2D images, however, are much harder to achieve for 3D scenes. Objects in 3D repositories like ShapeNet are labeled, but regrettably only in isolation, so without context. 3D scenes can be acquired by range scanners on city-level scale, but much fewer with semantic labels. Addressing this disparity, we introduce a new optimization procedure, which allows training for 3D detection with raw 3D scans while using as little as 5% of the object labels and still achieve comparable performance. Our optimization uses two networks. A scene network maps an entire 3D scene to a set of 3D object centers. As we assume the scene not to be labeled by centers, no classic loss, such as Chamfer can be used to train it. Instead, we use another network to emulate the loss. This loss network is trained on a small labeled subset and maps a non centered 3D object in the presence of distractions to its own center. This function is very similar - and hence can be used instead of - the gradient the supervised loss would provide. Our evaluation documents competitive fidelity at a much lower level of supervision, respectively higher quality at comparable supervision. Supplementary material can be found at: https://dgriffiths3.github.io.