A Spatiotemporal Approach to Tri-Perspective Representation for 3D Semantic Occupancy Prediction

• URL: http://arxiv.org/abs/2401.13785v3
• Date: Sun, 16 Feb 2025 10:14:02 GMT
• Title: A Spatiotemporal Approach to Tri-Perspective Representation for 3D Semantic Occupancy Prediction
• Authors: Sathira Silva, Savindu Bhashitha Wannigama, Gihan Jayatilaka, Muhammad Haris Khan, Roshan Ragel,
• Abstract summary: Vision-based 3D semantic occupancy prediction is increasingly overlooked in favor of LiDAR-based approaches. This study introduces S2TPVFormer, a transformer architecture designed to predict temporally coherent 3D semantic occupancy.
• Score: 6.527178779672975
• License:
• Abstract: Holistic understanding and reasoning in 3D scenes are crucial for the success of autonomous driving systems. The evolution of 3D semantic occupancy prediction as a pretraining task for autonomous driving and robotic applications captures finer 3D details compared to traditional 3D detection methods. Vision-based 3D semantic occupancy prediction is increasingly overlooked in favor of LiDAR-based approaches, which have shown superior performance in recent years. However, we present compelling evidence that there is still potential for enhancing vision-based methods. Existing approaches predominantly focus on spatial cues such as tri-perspective view (TPV) embeddings, often overlooking temporal cues. This study introduces S2TPVFormer, a spatiotemporal transformer architecture designed to predict temporally coherent 3D semantic occupancy. By introducing temporal cues through a novel Temporal Cross-View Hybrid Attention mechanism (TCVHA), we generate Spatiotemporal TPV (S2TPV) embeddings that enhance the prior process. Experimental evaluations on the nuScenes dataset demonstrate a significant +4.1% of absolute gain in mean Intersection over Union (mIoU) for 3D semantic occupancy compared to baseline TPVFormer, validating the effectiveness of S2TPVFormer in advancing 3D scene perception.

Related papers

• VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving [44.91443640710085]
  VisionPAD is a novel self-supervised pre-training paradigm for vision-centric algorithms in autonomous driving. It reconstructs multi-view representations using only images as supervision. It significantly improves performance in 3D object detection, occupancy prediction and map segmentation.
  arXiv  Detail & Related papers  (2024-11-22T03:59:41Z)
• FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View [46.81548000021799]
  In autonomous driving, 3D occupancy prediction outputs voxel-wise status and semantic labels for more comprehensive understandings of 3D scenes. Recent researchers have extensively explored various aspects of this task, including view transformation techniques, ground-truth label generation, and elaborate network design. A new method, dubbed FastOcc, is proposed to accelerate the model while keeping its accuracy. Experiments on the Occ3D-nuScenes benchmark demonstrate that our FastOcc achieves a fast inference speed.
  arXiv  Detail & Related papers  (2024-03-05T07:01:53Z)
• OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow [0.6577148087211809]
  We present a novel approach to occupancy estimation inspired by neural radiance field (NeRF) using only 2D labels. We employ differentiable volumetric rendering to predict depth and semantic maps and train a 3D network based on 2D supervision only.
  arXiv  Detail & Related papers  (2024-02-20T08:04:12Z)
• RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation [50.35403070279804]
  3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images. We propose RadOcc, a Rendering assisted distillation paradigm for 3D Occupancy prediction.
  arXiv  Detail & Related papers  (2023-12-19T03:39:56Z)
• 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)
• VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud [51.063494002003154]
  3D semantic scene graph (3DSSG) prediction in the point cloud is challenging since the 3D point cloud only captures geometric structures with limited semantics compared to 2D images. We propose Visual-Linguistic Semantics Assisted Training scheme that can significantly empower 3DSSG prediction models with discrimination about long-tailed and ambiguous semantic relations.
  arXiv  Detail & Related papers  (2023-03-25T09:14:18Z)
• ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning [132.20119288212376]
  We propose a spatial-temporal feature learning scheme towards a set of more representative features for perception, prediction and planning tasks simultaneously. To the best of our knowledge, we are the first to systematically investigate each part of an interpretable end-to-end vision-based autonomous driving system.
  arXiv  Detail & Related papers  (2022-07-15T16:57:43Z)
• On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation [57.766049538913926]
  Supervised approaches to 3D pose estimation from single images are remarkably effective when labeled data is abundant. Much of the recent attention has shifted towards semi and (or) weakly supervised learning. We propose to impose multi-view geometrical constraints by means of a differentiable triangulation and to use it as form of self-supervision during training when no labels are available.
  arXiv  Detail & Related papers  (2022-03-29T19:11:54Z)
• Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds [96.9027094562957]
  We introduce a-temporal representation learning framework, capable of learning from unlabeled tasks. Inspired by how infants learn from visual data in the wild, we explore rich cues derived from the 3D data. STRL takes two temporally-related frames from a 3D point cloud sequence as the input, transforms it with the spatial data augmentation, and learns the invariant representation self-supervisedly.
  arXiv  Detail & Related papers  (2021-09-01T04:17:11Z)

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.