More Like this

1. Underwater Depth Calibration Using a Commercial Depth Camera

   https://doi.org/10.1145/3567600.3568158  

   Wong, Emily; Humphrey, Isabella; Switzer, Scott; Crutchfield, Christopher; Hui, Nathan; Schurgers, Curt; Kastner, Ryan (November 2022, International Conference on Underwater Networks & Systems)
2. Depth Anyviewpoint? Evaluating Monocular Depth Over Viewpoint Sequences

   Rode, Theo; Zhang, Rui; Aubrey, Ryan; Li, Olivia; Huang, Jessica; Chu, Liv; Dowd, Healani; Wloka, Calden (October 2025, Southern California AI and Robotics Symposium 2025 (SCAIR25))
3. Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes

   Sun, Yihong; Hariharan, Bharath (December 2023, Conference on Neural Information Processing Systems)
4. Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes

   Sun, Yihong; Hariharan, Bharath (December 2023, Advances in neural information processing systems)

   Unsupervised monocular depth estimation techniques have demonstrated encour- aging results but typically assume that the scene is static. These techniques suffer when trained on dynamical scenes, where apparent object motion can equally be ex- plained by hypothesizing the object’s independent motion, or by altering its depth. This ambiguity causes depth estimators to predict erroneous depth for moving objects. To resolve this issue, we introduce Dynamo-Depth, an unifying approach that disambiguates dynamical motion by jointly learning monocular depth, 3D independent flow field, and motion segmentation from unlabeled monocular videos. Specifically, we offer our key insight that a good initial estimation of motion seg- mentation is sufficient for jointly learning depth and independent motion despite the fundamental underlying ambiguity. Our proposed method achieves state-of-the-art performance on monocular depth estimation on Waymo Open [34] and nuScenes [3] Dataset with significant improvement in the depth of moving objects. Code and additional results are available at https://dynamo-depth.github.io. 

   more » « less
5. Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes

   Sun, Yihong; Hariharan, Bharath (December 2023, Advances in Neural Information Processing Systems 36 (NeurIPS 2023))

   Unsupervised monocular depth estimation techniques have demonstrated encouraging results but typically assume that the scene is static. These techniques suffer when trained on dynamical scenes, where apparent object motion can equally be explained by hypothesizing the object's independent motion, or by altering its depth. This ambiguity causes depth estimators to predict erroneous depth for moving objects. To resolve this issue, we introduce Dynamo-Depth, an unifying approach that disambiguates dynamical motion by jointly learning monocular depth, 3D independent flow field, and motion segmentation from unlabeled monocular videos. Specifically, we offer our key insight that a good initial estimation of motion segmentation is sufficient for jointly learning depth and independent motion despite the fundamental underlying ambiguity. Our proposed method achieves state-of-the-art performance on monocular depth estimation on Waymo Open and nuScenes Dataset with significant improvement in the depth of moving objects. Code and additional results are available at https://dynamo-depth.github.io. 

   more » « less