Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation
- URL: http://arxiv.org/abs/2411.12177v1
- Date: Tue, 19 Nov 2024 02:40:42 GMT
- Title: Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation
- Authors: Zhuangwei Zhuang, Ziyin Wang, Sitao Chen, Lizhao Liu, Hui Luo, Mingkui Tan,
- Abstract summary: For autonomous cars equipped with multi-camera and LiDAR, it is critical to aggregate multi-sensor information into a unified 3D space for accurate and robust predictions.
Recent methods are mainly built on the 2D-to-3D transformation that relies on sensor calibration to project the 2D image information into the 3D space.
In this work, we propose a calibration-free spatial transformation based on vanilla attention to implicitly model the spatial correspondence.
- Score: 32.50849425431012
- License:
- Abstract: 3D semantic occupancy prediction, which seeks to provide accurate and comprehensive representations of environment scenes, is important to autonomous driving systems. For autonomous cars equipped with multi-camera and LiDAR, it is critical to aggregate multi-sensor information into a unified 3D space for accurate and robust predictions. Recent methods are mainly built on the 2D-to-3D transformation that relies on sensor calibration to project the 2D image information into the 3D space. These methods, however, suffer from two major limitations: First, they rely on accurate sensor calibration and are sensitive to the calibration noise, which limits their application in real complex environments. Second, the spatial transformation layers are computationally expensive and limit their running on an autonomous vehicle. In this work, we attempt to exploit a Robust and Efficient 3D semantic Occupancy (REO) prediction scheme. To this end, we propose a calibration-free spatial transformation based on vanilla attention to implicitly model the spatial correspondence. In this way, we robustly project the 2D features to a predefined BEV plane without using sensor calibration as input. Then, we introduce 2D and 3D auxiliary training tasks to enhance the discrimination power of 2D backbones on spatial, semantic, and texture features. Last, we propose a query-based prediction scheme to efficiently generate large-scale fine-grained occupancy predictions. By fusing point clouds that provide complementary spatial information, our REO surpasses the existing methods by a large margin on three benchmarks, including OpenOccupancy, Occ3D-nuScenes, and SemanticKITTI Scene Completion. For instance, our REO achieves 19.8$\times$ speedup compared to Co-Occ, with 1.1 improvements in geometry IoU on OpenOccupancy. Our code will be available at https://github.com/ICEORY/REO.
Related papers
- GaussRender: Learning 3D Occupancy with Gaussian Rendering [84.60008381280286]
GaussRender is a plug-and-play 3D-to-2D reprojection loss that enhances voxel-based supervision.
Our method projects 3D voxel representations into arbitrary 2D perspectives and leverages Gaussian splatting as an efficient, differentiable rendering proxy of voxels.
arXiv Detail & Related papers (2025-02-07T16:07:51Z) - Lift3D Foundation Policy: Lifting 2D Large-Scale Pretrained Models for Robust 3D Robotic Manipulation [30.744137117668643]
Lift3D is a framework that enhances 2D foundation models with implicit and explicit 3D robotic representations to construct a robust 3D manipulation policy.
In experiments, Lift3D consistently outperforms previous state-of-the-art methods across several simulation benchmarks and real-world scenarios.
arXiv Detail & Related papers (2024-11-27T18:59:52Z) - ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction [89.89610257714006]
Existing methods prioritize higher accuracy to cater to the demands of these tasks.
We introduce a series of targeted improvements for 3D semantic occupancy prediction and flow estimation.
Our purelytemporalal architecture framework, named ALOcc, achieves an optimal tradeoff between speed and accuracy.
arXiv Detail & Related papers (2024-11-12T11:32:56Z) - NDC-Scene: Boost Monocular 3D Semantic Scene Completion in Normalized
Device Coordinates Space [77.6067460464962]
Monocular 3D Semantic Scene Completion (SSC) has garnered significant attention in recent years due to its potential to predict complex semantics and geometry shapes from a single image, requiring no 3D inputs.
We identify several critical issues in current state-of-the-art methods, including the Feature Ambiguity of projected 2D features in the ray to the 3D space, the Pose Ambiguity of the 3D convolution, and the Imbalance in the 3D convolution across different depth levels.
We devise a novel Normalized Device Coordinates scene completion network (NDC-Scene) that directly extends the 2
arXiv Detail & Related papers (2023-09-26T02:09:52Z) - Uncertainty-aware State Space Transformer for Egocentric 3D Hand
Trajectory Forecasting [79.34357055254239]
Hand trajectory forecasting is crucial for enabling a prompt understanding of human intentions when interacting with AR/VR systems.
Existing methods handle this problem in a 2D image space which is inadequate for 3D real-world applications.
We set up an egocentric 3D hand trajectory forecasting task that aims to predict hand trajectories in a 3D space from early observed RGB videos in a first-person view.
arXiv Detail & Related papers (2023-07-17T04:55:02Z) - Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation [18.964403296437027]
Act3D represents the robot's workspace using a 3D feature field with adaptive resolutions dependent on the task at hand.
It samples 3D point grids in a coarse to fine manner, featurizes them using relative-position attention, and selects where to focus the next round of point sampling.
arXiv Detail & Related papers (2023-06-30T17:34:06Z) - Uncertainty-Aware Camera Pose Estimation from Points and Lines [101.03675842534415]
Perspective-n-Point-and-Line (Pn$PL) aims at fast, accurate and robust camera localizations with respect to a 3D model from 2D-3D feature coordinates.
arXiv Detail & Related papers (2021-07-08T15:19:36Z) - PLUME: Efficient 3D Object Detection from Stereo Images [95.31278688164646]
Existing methods tackle the problem in two steps: first depth estimation is performed, a pseudo LiDAR point cloud representation is computed from the depth estimates, and then object detection is performed in 3D space.
We propose a model that unifies these two tasks in the same metric space.
Our approach achieves state-of-the-art performance on the challenging KITTI benchmark, with significantly reduced inference time compared with existing methods.
arXiv Detail & Related papers (2021-01-17T05:11:38Z)
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.