Lifting Unlabeled Internet-level Data for 3D Scene Understanding

• URL: http://arxiv.org/abs/2604.01907v1
• Date: Thu, 02 Apr 2026 11:26:44 GMT
• Title: Lifting Unlabeled Internet-level Data for 3D Scene Understanding
• Abstract summary: We demonstrate that carefully designed data engines can leverage web-curated, unlabeled videos to automatically generate training data.<n>We identify and analyze bottlenecks in automated data generation, revealing critical factors that determine the efficiency and effectiveness of learning from unlabeled data.
• Score: 56.35787129098819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Annotated 3D scene data is scarce and expensive to acquire, while abundant unlabeled videos are readily available on the internet. In this paper, we demonstrate that carefully designed data engines can leverage web-curated, unlabeled videos to automatically generate training data, to facilitate end-to-end models in 3D scene understanding alongside human-annotated datasets. We identify and analyze bottlenecks in automated data generation, revealing critical factors that determine the efficiency and effectiveness of learning from unlabeled data. To validate our approach across different perception granularities, we evaluate on three tasks spanning low-level perception, i.e., 3D object detection and instance segmentation, to high-evel reasoning, i.e., 3D spatial Visual Question Answering (VQA) and Vision-Lanugage Navigation (VLN). Models trained on our generated data demonstrate strong zero-shot performance and show further improvement after finetuning. This demonstrates the viability of leveraging readily available web data as a path toward more capable scene understanding systems.

Related papers

• Few-Shot Learning in Video and 3D Object Detection: A Survey [1.8507330561392012]
  Few-shot learning enables object detection models to recognize novel classes given only a few examples.<n>Few-shot 3D detection enables practical autonomous driving deployment by minimizing costly 3D annotation needs.
  arXiv  Detail & Related papers  (2025-07-22T23:37:20Z)
• Open-Vocabulary High-Resolution 3D (OVHR3D) Data Segmentation and Annotation Framework [1.1280113914145702]
  This research aims to design and develop a comprehensive and efficient framework for 3D segmentation tasks.<n>The framework integrates Grounding DINO and Segment anything Model, augmented by an enhancement in 2D image rendering via 3D mesh.
  arXiv  Detail & Related papers  (2024-12-09T07:39:39Z)
• Articulate3D: Holistic Understanding of 3D Scenes as Universal Scene Description [56.69740649781989]
  3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI.<n>We introduce Articulate3D, an expertly curated 3D dataset featuring high-quality manual annotations on 280 indoor scenes.<n>We also present USDNet, a novel unified framework capable of simultaneously predicting part segmentation along with a full specification of motion attributes for articulated objects.
  arXiv  Detail & Related papers  (2024-12-02T11:33:55Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• AutoDecoding Latent 3D Diffusion Models [95.7279510847827]
  We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core. The 3D autodecoder framework embeds properties learned from the target dataset in the latent space. We then identify the appropriate intermediate volumetric latent space, and introduce robust normalization and de-normalization operations.
  arXiv  Detail & Related papers  (2023-07-07T17:59:14Z)
• 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone [10.341296683155973]
  We propose using a self-supervised training strategy to learn a general point cloud backbone model for downstream 3D vision tasks. Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a 3D detection head. Experiments on KITTI and nuScenes benchmarks show that the proposed self-supervised pre-training increases 3D detection performance significantly.
  arXiv  Detail & Related papers  (2022-05-02T07:53:29Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Uncertainty-aware Self-supervised 3D Data Association [20.853544785595343]
  We propose leveraging vast unlabeled datasets by self-supervised metric learning of 3D object trackers. We show how these self-supervised annotations can be used in a principled manner to learn point-cloud embeddings. We design embeddings to differentiate objects across frames, and learn them using uncertainty-aware self-supervised training.
  arXiv  Detail & Related papers  (2020-08-18T22:34:07Z)
• PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding [107.02479689909164]
  In this work, we aim at facilitating research on 3D representation learning. We measure the effect of unsupervised pre-training on a large source set of 3D scenes.
  arXiv  Detail & Related papers  (2020-07-21T17:59:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.