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GaussianOcc: Fully Self-supervised and Efficient 3D Occupancy Estimation with Gaussian Splatting

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arxiv 2408.11447 v4 pith:NIU7ZHI6 submitted 2024-08-21 cs.CV

Wanshui Gan , Fang Liu , Hongbin Xu , Ningkai Mo , Naoto Yokoya This is my paper · ORCID
classification cs.CV
keywords gaussianself-supervisedsplattingestimationfullygaussianoccoccupancyrendering
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We introduce GaussianOcc, a systematic method that investigates the two usages of Gaussian splatting for fully self-supervised and efficient 3D occupancy estimation in surround views. First, traditional methods for self-supervised 3D occupancy estimation still require ground truth 6D poses from sensors during training. To address this limitation, we propose Gaussian Splatting for Projection (GSP) module to provide accurate scale information for fully self-supervised training from adjacent view projection. Additionally, existing methods rely on volume rendering for final 3D voxel representation learning using 2D signals (depth maps, semantic maps), which is both time-consuming and less effective. We propose Gaussian Splatting from Voxel space (GSV) to leverage the fast rendering properties of Gaussian splatting. As a result, the proposed GaussianOcc method enables fully self-supervised (no ground truth pose) 3D occupancy estimation in competitive performance with low computational cost (2.7 times faster in training and 5 times faster in rendering). The relevant code is available in https://github.com/GANWANSHUI/GaussianOcc.git.

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VoxelSplat: Dynamic Gaussian Splatting as an Effective Loss for Occupancy and Flow Prediction

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A training-only Gaussian splatting loss, which renders predicted 3D semantics and motion into 2D camera views, improves semantic occupancy and scene flow prediction across several camera-based models.

  2. Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction

    cs.CV 2025-04 conditional novelty 6.0 of 10

    GDFusion fuses scene, motion, and geometry cues through gradient-descent-style RNN updates, improving mIoU by 1.4 to 4.8 points on Occ3D while cutting inference memory by 27 to 72 percent.

  3. GaussRender: Learning 3D Occupancy with Gaussian Rendering

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A plug-and-play Gaussian-splatting rendering loss that supervises 3D occupancy predictions through 2D depth and semantic images improves geometric fidelity and surface-sensitive metrics across multiple models and benchmarks.

  4. GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial Understanding

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A sparse Gaussian Transformer aligned with 2D foundation models achieves 12.27 mIoU zero-shot on Occ3D-nuScenes occupancy prediction without 3D semantic labels.

  5. VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving

    cs.CV 2024-11 conditional novelty 6.0 of 10

    VisionPAD uses 3D Gaussian Splatting, self-supervised voxel velocity estimation, and photometric consistency to pre-train vision-centric driving models from images only.

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