10 citations · 10 across the 4 of their papers we have counts for
5 papers · 1 filter
ODG: Occupancy Prediction Using Dual Gaussians
Yunxiao Shi, Yinhao Zhu, Shizhong Han +4
Occupancy prediction infers fine-grained 3D geometry and semantics from camera images of the surrounding environment, making it a critical perception task for autonomous driving. E…
BePo: Dual Representation for 3D Occupancy Prediction
Yunxiao Shi, Hong Cai, Jisoo Jeong +4
3D occupancy infers fine-grained 3D geometry and semantics which is critical for autonomous driving. Most existing approaches carry high compute costs, requiring dense 3D feature v…
H3O: Hyper-Efficient 3D Occupancy Prediction with Heterogeneous Supervision
Yunxiao Shi, Hong Cai, Amin Ansari +1
3D occupancy prediction has recently emerged as a new paradigm for holistic 3D scene understanding and provides valuable information for downstream planning in autonomous driving.…
EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation
Yunxiao Shi, Hong Cai, Amin Ansari +1
The ubiquitous multi-camera setup on modern autonomous vehicles provides an opportunity to construct surround-view depth. Existing methods, however, either perform independent mono…
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
Hong Cai, Janarbek Matai, Shubhankar Borse +3
In this paper, we propose a novel method, X-Distill, to improve the self-supervised training of monocular depth via cross-task knowledge distillation from semantic segmentation to…