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20182022
most citedTime Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection

71 citations · 264 across the 22 of their papers we have counts for

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27 papers · 1 filter

cs.CV20224 cited

3D-Aware Encoding for Style-based Neural Radiance Fields

Yu-Jhe Li, Tao Xu, Bichen Wu +6

We tackle the task of NeRF inversion for style-based neural radiance fields, (e.g., StyleNeRF). In the task, we aim to learn an inversion function to project an input image to the…

cs.CV20229 cited

Track Targets by Dense Spatio-Temporal Position Encoding

Jinkun Cao, Hao Wu, Kris Kitani

In this work, we propose a novel paradigm to encode the position of targets for target tracking in videos using transformers. The proposed paradigm, Dense Spatio-Temporal (DST) pos…

cs.CV202271 cited

Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection

Jinhyung Park, Chenfeng Xu, Shijia Yang +4

While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object…

cs.CV2022

Occluded Human Mesh Recovery

Rawal Khirodkar, Shashank Tripathi, Kris Kitani

Top-down methods for monocular human mesh recovery have two stages: (1) detect human bounding boxes; (2) treat each bounding box as an independent single-human mesh recovery task.…

cs.CV2021

Multi-Echo LiDAR for 3D Object Detection

Yunze Man, Xinshuo Weng, Prasanna Kumar Sivakuma +2

LiDAR sensors can be used to obtain a wide range of measurement signals other than a simple 3D point cloud, and those signals can be leveraged to improve perception tasks like 3D o…

cs.CV20215 cited

Multi-Modality Task Cascade for 3D Object Detection

Jinhyung Park, Xinshuo Weng, Yunze Man +1

Point clouds and RGB images are naturally complementary modalities for 3D visual understanding - the former provides sparse but accurate locations of points on objects, while the l…