1. Neural Fields meet Explicit Geometric Representations for Inverse Rendering of Urban Scenes 

   Wang, Zian; Shen, Tianchang; Gao, Jun; et al. (2023)

   2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)

   Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods achieve impressive fidelity of 3D reconstruction, but bake the lighting and shadows into the radiance field, while mesh-based methods that facilitate intrinsic decomposition through differentiable rendering have not yet scaled to the complexity and scale of ...

   Conference Paper

2. Indiscernible Object Counting in Underwater Scenes 

   Sun, Guolei; An, Zhaochong; Liu, Yun; et al. (2023)

   2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)

   Recently, indiscernible scene understanding has attracted a lot of attention in the vision community. We further advance the frontier of this field by systematically studying a new challenge named indiscernible object counting (IOC), the goal of which is to count objects that are blended with respect to their surroundings. Due to a lack of appropriate IOC datasets, we present a large-scale dataset IOCfish5K which contains a total of 5,637 ...

   Conference Paper

3. BITE: Beyond Priors for Improved Three-D Dog Pose Estimation 

   Ruegg, Nadine; Tripathi, Shashank; Schindler, Konrad; et al. (2023)

   2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)

   We address the problem of inferring the 3D shape and pose of dogs from images. Given the lack of 3D training data, this problem is challenging, and the best methods lag behind those designed to estimate human shape and pose. To make progress, we attack the problem from multiple sides at once. First, we need a good 3D shape prior, like those available for humans. To that end, we learn a dog-specific 3D parametric model, called D-SMAL. ...

   Conference Paper

4. MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation 

   Hoyer, Lukas; Dai, Dengxin; Wang, Haoran; et al. (2023)

   2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)

   In unsupervised domain adaptation (UDA), a model trained on source data (e.g. synthetic) is adapted to target data (e.g. real-world) without access to target annotation. Most previous UDA methods struggle with classes that have a similar visual appearance on the target domain as no ground truth is available to learn the slight appearance differences. To address this problem, we propose a Masked Image Consistency (MIC) module to enhance ...

   Conference Paper

5. Learning Human-to-Robot Handovers from Point Clouds 

   Christen, Sammy; Yang, Wei; Perez-D'Arpino, Claudia; et al. (2023)

   2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)

   We propose the first framework to learn control policies for vision-based human-to-robot handovers, a critical task for human-robot interaction. While research in Embodied AI has made significant progress in training robot agents in simulated environments, interacting with humans remains challenging due to the difficulties of simulating humans. Fortunately, recent research has developed realistic simulated environments for human-to-robot ...

   Conference Paper

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